{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3D Multi-organ Segmentation with UNETR  (BTCV Challenge)\n",
    "\n",
    "Prepared by: **Ali Hatamizadeh** and **Yucheng Tang**.\n",
    "\n",
    "This tutorial demonstrates how to construct a training workflow of UNETR on multi-organ segmentation task using the BTCV challenge dataset.\n",
    "![image](https://lh3.googleusercontent.com/pw/AM-JKLU2eTW17rYtCmiZP3WWC-U1HCPOHwLe6pxOfJXwv2W-00aHfsNy7jeGV1dwUq0PXFOtkqasQ2Vyhcu6xkKsPzy3wx7O6yGOTJ7ZzA01S6LSh8szbjNLfpbuGgMe6ClpiS61KGvqu71xXFnNcyvJNFjN=w1448-h496-no?authuser=0)\n",
    "\n",
    "And it contains the following features:\n",
    "1. Transforms for dictionary format data.\n",
    "1. Define a new transform according to MONAI transform API.\n",
    "1. Load Nifti image with metadata, load a list of images and stack them.\n",
    "1. Randomly adjust intensity for data augmentation.\n",
    "1. Cache IO and transforms to accelerate training and validation.\n",
    "1. 3D UNETR model, DiceCE loss function, Mean Dice metric for multi-organ segmentation task.\n",
    "\n",
    "The dataset comes from https://www.synapse.org/#!Synapse:syn3193805/wiki/217752.  \n",
    "\n",
    "Under Institutional Review Board (IRB) supervision, 50 abdomen CT scans of were randomly selected from a combination of an ongoing colorectal cancer chemotherapy trial, and a retrospective ventral hernia study. The 50 scans were captured during portal venous contrast phase with variable volume sizes (512 x 512 x 85 - 512 x 512 x 198) and field of views (approx. 280 x 280 x 280 mm3 - 500 x 500 x 650 mm3). The in-plane resolution varies from 0.54 x 0.54 mm2 to 0.98 x 0.98 mm2, while the slice thickness ranges from 2.5 mm to 5.0 mm. \n",
    "\n",
    "Target: 13 abdominal organs including 1. Spleen 2. Right Kidney 3. Left Kideny 4.Gallbladder 5.Esophagus 6. Liver 7. Stomach 8.Aorta 9. IVC 10. Portal and Splenic Veins 11. Pancreas 12 Right adrenal gland 13 Left adrenal gland.\n",
    "\n",
    "Modality: CT\n",
    "Size: 30 3D volumes (24 Training + 6 Testing)  \n",
    "Challenge: BTCV MICCAI Challenge\n",
    "\n",
    "The following figure shows image patches with the organ sub-regions that are annotated in the CT (top left) and the final labels for the whole dataset (right).\n",
    "\n",
    "Data, figures and resources are taken from: \n",
    "\n",
    "\n",
    "1. [UNETR: Transformers for 3D Medical Image Segmentation](https://arxiv.org/abs/2103.10504)\n",
    "\n",
    "2. [High-resolution 3D abdominal segmentation with random patch network fusion (MIA)](https://www.sciencedirect.com/science/article/abs/pii/S1361841520302589)\n",
    "\n",
    "3. [Efficient multi-atlas abdominal segmentation on clinically acquired CT with SIMPLE context learning (MIA)](https://www.sciencedirect.com/science/article/abs/pii/S1361841515000766?via%3Dihub)\n",
    "\n",
    "\n",
    "![image](https://lh3.googleusercontent.com/pw/AM-JKLX0svvlMdcrchGAgiWWNkg40lgXYjSHsAAuRc5Frakmz2pWzSzf87JQCRgYpqFR0qAjJWPzMQLc_mmvzNjfF9QWl_1OHZ8j4c9qrbR6zQaDJWaCLArRFh0uPvk97qAa11HtYbD6HpJ-wwTCUsaPcYvM=w1724-h522-no?authuser=0)\n",
    "\n",
    "\n",
    "\n",
    "The image patches show anatomies of a subject, including: \n",
    "1. large organs: spleen, liver, stomach. \n",
    "2. Smaller organs: gallbladder, esophagus, kidneys, pancreas. \n",
    "3. Vascular tissues: aorta, IVC, P&S Veins. \n",
    "4. Glands: left and right adrenal gland"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -q \"monai-weekly[nibabel, tqdm, einops]\"\n",
    "!python -c \"import matplotlib\" || pip install -q matplotlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MONAI version: 0.7.dev2128\n",
      "Numpy version: 1.19.2\n",
      "Pytorch version: 1.8.0a0+17f8c32\n",
      "MONAI flags: HAS_EXT = False, USE_COMPILED = False\n",
      "MONAI rev id: 7e0be5ff877310fd864c8b615a334e631671ca0c\n",
      "\n",
      "Optional dependencies:\n",
      "Pytorch Ignite version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "Nibabel version: 3.2.1\n",
      "scikit-image version: 0.15.0\n",
      "Pillow version: 7.0.0.post3\n",
      "Tensorboard version: 1.15.0+nv\n",
      "gdown version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "TorchVision version: 0.8.0a0\n",
      "ITK version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "tqdm version: 4.61.2\n",
      "lmdb version: 1.0.0\n",
      "psutil version: 5.7.2\n",
      "pandas version: 0.24.2\n",
      "einops version: 0.3.0\n",
      "\n",
      "For details about installing the optional dependencies, please visit:\n",
      "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import shutil\n",
    "import tempfile\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "\n",
    "from monai.losses import DiceCELoss\n",
    "from monai.inferers import sliding_window_inference\n",
    "from monai.transforms import (\n",
    "    AsDiscrete,\n",
    "    AddChanneld,\n",
    "    Compose,\n",
    "    CropForegroundd,\n",
    "    LoadImaged,\n",
    "    Orientationd,\n",
    "    RandFlipd,\n",
    "    RandCropByPosNegLabeld,\n",
    "    RandShiftIntensityd,\n",
    "    ScaleIntensityRanged,\n",
    "    Spacingd,\n",
    "    RandRotate90d,\n",
    "    ToTensord,\n",
    ")\n",
    "\n",
    "from monai.config import print_config\n",
    "from monai.metrics import DiceMetric\n",
    "from monai.networks.nets import UNETR\n",
    "\n",
    "from monai.data import (\n",
    "    DataLoader,\n",
    "    CacheDataset,\n",
    "    load_decathlon_datalist,\n",
    "    decollate_batch,\n",
    ")\n",
    "\n",
    "\n",
    "import torch\n",
    "\n",
    "print_config()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup data directory\n",
    "\n",
    "You can specify a directory with the `MONAI_DATA_DIRECTORY` environment variable.  \n",
    "This allows you to save results and reuse downloads.  \n",
    "If not specified a temporary directory will be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/tmp/tmpaac6jn29\n"
     ]
    }
   ],
   "source": [
    "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
    "root_dir = tempfile.mkdtemp() if directory is None else directory\n",
    "print(root_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup transforms for training and validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_transforms = Compose(\n",
    "    [\n",
    "        LoadImaged(keys=[\"image\", \"label\"]),\n",
    "        AddChanneld(keys=[\"image\", \"label\"]),\n",
    "        Spacingd(\n",
    "            keys=[\"image\", \"label\"],\n",
    "            pixdim=(1.5, 1.5, 2.0),\n",
    "            mode=(\"bilinear\", \"nearest\"),\n",
    "        ),\n",
    "        Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
    "        ScaleIntensityRanged(\n",
    "            keys=[\"image\"],\n",
    "            a_min=-175,\n",
    "            a_max=250,\n",
    "            b_min=0.0,\n",
    "            b_max=1.0,\n",
    "            clip=True,\n",
    "        ),\n",
    "        CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\"),\n",
    "        RandCropByPosNegLabeld(\n",
    "            keys=[\"image\", \"label\"],\n",
    "            label_key=\"label\",\n",
    "            spatial_size=(96, 96, 96),\n",
    "            pos=1,\n",
    "            neg=1,\n",
    "            num_samples=4,\n",
    "            image_key=\"image\",\n",
    "            image_threshold=0,\n",
    "        ),\n",
    "        RandFlipd(\n",
    "            keys=[\"image\", \"label\"],\n",
    "            spatial_axis=[0],\n",
    "            prob=0.10,\n",
    "        ),\n",
    "        RandFlipd(\n",
    "            keys=[\"image\", \"label\"],\n",
    "            spatial_axis=[1],\n",
    "            prob=0.10,\n",
    "        ),\n",
    "        RandFlipd(\n",
    "            keys=[\"image\", \"label\"],\n",
    "            spatial_axis=[2],\n",
    "            prob=0.10,\n",
    "        ),\n",
    "        RandRotate90d(\n",
    "            keys=[\"image\", \"label\"],\n",
    "            prob=0.10,\n",
    "            max_k=3,\n",
    "        ),\n",
    "        RandShiftIntensityd(\n",
    "            keys=[\"image\"],\n",
    "            offsets=0.10,\n",
    "            prob=0.50,\n",
    "        ),\n",
    "        ToTensord(keys=[\"image\", \"label\"]),\n",
    "    ]\n",
    ")\n",
    "val_transforms = Compose(\n",
    "    [\n",
    "        LoadImaged(keys=[\"image\", \"label\"]),\n",
    "        AddChanneld(keys=[\"image\", \"label\"]),\n",
    "        Spacingd(\n",
    "            keys=[\"image\", \"label\"],\n",
    "            pixdim=(1.5, 1.5, 2.0),\n",
    "            mode=(\"bilinear\", \"nearest\"),\n",
    "        ),\n",
    "        Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
    "        ScaleIntensityRanged(\n",
    "            keys=[\"image\"], a_min=-175, a_max=250, b_min=0.0, b_max=1.0, clip=True\n",
    "        ),\n",
    "        CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\"),\n",
    "        ToTensord(keys=[\"image\", \"label\"]),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Download dataset and format in the folder.\n",
    "    1. Download dataset from here: https://www.synapse.org/#!Synapse:syn3193805/wiki/89480\\n\n",
    "    2. Put images in the ./data/imagesTr\n",
    "    3. Put labels in the ./data/labelsTr\n",
    "    4. make JSON file accordingly: ./data/dataset_0.json\n",
    "    Example of JSON file:\n",
    "     {\n",
    "    \"description\": \"btcv yucheng\",\n",
    "    \"labels\": {\n",
    "        \"0\": \"background\",\n",
    "        \"1\": \"spleen\",\n",
    "        \"2\": \"rkid\",\n",
    "        \"3\": \"lkid\",\n",
    "        \"4\": \"gall\",\n",
    "        \"5\": \"eso\",\n",
    "        \"6\": \"liver\",\n",
    "        \"7\": \"sto\",\n",
    "        \"8\": \"aorta\",\n",
    "        \"9\": \"IVC\",\n",
    "        \"10\": \"veins\",\n",
    "        \"11\": \"pancreas\",\n",
    "        \"12\": \"rad\",\n",
    "        \"13\": \"lad\"\n",
    "    },\n",
    "    \"licence\": \"yt\",\n",
    "    \"modality\": {\n",
    "        \"0\": \"CT\"\n",
    "    },\n",
    "    \"name\": \"btcv\",\n",
    "    \"numTest\": 20,\n",
    "    \"numTraining\": 80,\n",
    "    \"reference\": \"Vanderbilt University\",\n",
    "    \"release\": \"1.0 06/08/2015\",\n",
    "    \"tensorImageSize\": \"3D\",\n",
    "    \"test\": [\n",
    "        \"imagesTs/img0061.nii.gz\",\n",
    "        \"imagesTs/img0062.nii.gz\",\n",
    "        \"imagesTs/img0063.nii.gz\",\n",
    "        \"imagesTs/img0064.nii.gz\",\n",
    "        \"imagesTs/img0065.nii.gz\",\n",
    "        \"imagesTs/img0066.nii.gz\",\n",
    "        \"imagesTs/img0067.nii.gz\",\n",
    "        \"imagesTs/img0068.nii.gz\",\n",
    "        \"imagesTs/img0069.nii.gz\",\n",
    "        \"imagesTs/img0070.nii.gz\",\n",
    "        \"imagesTs/img0071.nii.gz\",\n",
    "        \"imagesTs/img0072.nii.gz\",\n",
    "        \"imagesTs/img0073.nii.gz\",\n",
    "        \"imagesTs/img0074.nii.gz\",\n",
    "        \"imagesTs/img0075.nii.gz\",\n",
    "        \"imagesTs/img0076.nii.gz\",\n",
    "        \"imagesTs/img0077.nii.gz\",\n",
    "        \"imagesTs/img0078.nii.gz\",\n",
    "        \"imagesTs/img0079.nii.gz\",\n",
    "        \"imagesTs/img0080.nii.gz\"\n",
    "    ],\n",
    "    \"training\": [\n",
    "        {\n",
    "            \"image\": \"imagesTr/img0001.nii.gz\",\n",
    "            \"label\": \"labelsTr/label0001.nii.gz\"\n",
    "        },\n",
    "        {\n",
    "            \"image\": \"imagesTr/img0002.nii.gz\",\n",
    "            \"label\": \"labelsTr/label0002.nii.gz\"\n",
    "        },\n",
    "        {\n",
    "            \"image\": \"imagesTr/img0003.nii.gz\",\n",
    "            \"label\": \"labelsTr/label0003.nii.gz\"\n",
    "        },\n",
    "        {\n",
    "            \"image\": \"imagesTr/img0004.nii.gz\",\n",
    "            \"label\": \"labelsTr/label0004.nii.gz\"\n",
    "        },\n",
    "        {\n",
    "            \"image\": \"imagesTr/img0005.nii.gz\",\n",
    "            \"label\": \"labelsTr/label0005.nii.gz\"\n",
    "        },\n",
    "        {\n",
    "            \"image\": \"imagesTr/img0006.nii.gz\",\n",
    "            \"label\": \"labelsTr/label0006.nii.gz\"\n",
    "        },\n",
    "        {\n",
    "            \"image\": \"imagesTr/img0007.nii.gz\",\n",
    "            \"label\": \"labelsTr/label0007.nii.gz\"\n",
    "        },\n",
    "        {\n",
    "            \"image\": \"imagesTr/img0008.nii.gz\",\n",
    "            \"label\": \"labelsTr/label0008.nii.gz\"\n",
    "        },\n",
    "        {\n",
    "            \"image\": \"imagesTr/img0009.nii.gz\",\n",
    "            \"label\": \"labelsTr/label0009.nii.gz\"\n",
    "        },\n",
    "        {\n",
    "            \"image\": \"imagesTr/img0010.nii.gz\",\n",
    "            \"label\": \"labelsTr/label0010.nii.gz\"\n",
    "        },\n",
    "        {\n",
    "            \"image\": \"imagesTr/img0021.nii.gz\",\n",
    "            \"label\": \"labelsTr/label0021.nii.gz\"\n",
    "        },\n",
    "        {\n",
    "            \"image\": \"imagesTr/img0022.nii.gz\",\n",
    "            \"label\": \"labelsTr/label0022.nii.gz\"\n",
    "        },\n",
    "        {\n",
    "            \"image\": \"imagesTr/img0023.nii.gz\",\n",
    "            \"label\": \"labelsTr/label0023.nii.gz\"\n",
    "        },\n",
    "        {\n",
    "            \"image\": \"imagesTr/img0024.nii.gz\",\n",
    "            \"label\": \"labelsTr/label0024.nii.gz\"\n",
    "        },\n",
    "        {\n",
    "            \"image\": \"imagesTr/img0025.nii.gz\",\n",
    "            \"label\": \"labelsTr/label0025.nii.gz\"\n",
    "        },\n",
    "        {\n",
    "            \"image\": \"imagesTr/img0026.nii.gz\",\n",
    "            \"label\": \"labelsTr/label0026.nii.gz\"\n",
    "        },\n",
    "        {\n",
    "            \"image\": \"imagesTr/img0027.nii.gz\",\n",
    "            \"label\": \"labelsTr/label0027.nii.gz\"\n",
    "        },\n",
    "        {\n",
    "            \"image\": \"imagesTr/img0028.nii.gz\",\n",
    "            \"label\": \"labelsTr/label0028.nii.gz\"\n",
    "        },\n",
    "        {\n",
    "            \"image\": \"imagesTr/img0029.nii.gz\",\n",
    "            \"label\": \"labelsTr/label0029.nii.gz\"\n",
    "        },\n",
    "        {\n",
    "            \"image\": \"imagesTr/img0030.nii.gz\",\n",
    "            \"label\": \"labelsTr/label0030.nii.gz\"\n",
    "        },\n",
    "        {\n",
    "            \"image\": \"imagesTr/img0031.nii.gz\",\n",
    "            \"label\": \"labelsTr/label0031.nii.gz\"\n",
    "        },\n",
    "        {\n",
    "            \"image\": \"imagesTr/img0032.nii.gz\",\n",
    "            \"label\": \"labelsTr/label0032.nii.gz\"\n",
    "        },\n",
    "        {\n",
    "            \"image\": \"imagesTr/img0033.nii.gz\",\n",
    "            \"label\": \"labelsTr/label0033.nii.gz\"\n",
    "        },\n",
    "        {\n",
    "            \"image\": \"imagesTr/img0034.nii.gz\",\n",
    "            \"label\": \"labelsTr/label0034.nii.gz\"\n",
    "        }\n",
    "    ],\n",
    "    \"validation\": [\n",
    "        {\n",
    "            \"image\": \"imagesTr/img0035.nii.gz\",\n",
    "            \"label\": \"labelsTr/label0035.nii.gz\"\n",
    "        },\n",
    "        {\n",
    "            \"image\": \"imagesTr/img0036.nii.gz\",\n",
    "            \"label\": \"labelsTr/label0036.nii.gz\"\n",
    "        },\n",
    "        {\n",
    "            \"image\": \"imagesTr/img0037.nii.gz\",\n",
    "            \"label\": \"labelsTr/label0037.nii.gz\"\n",
    "        },\n",
    "        {\n",
    "            \"image\": \"imagesTr/img0038.nii.gz\",\n",
    "            \"label\": \"labelsTr/label0038.nii.gz\"\n",
    "        },\n",
    "        {\n",
    "            \"image\": \"imagesTr/img0039.nii.gz\",\n",
    "            \"label\": \"labelsTr/label0039.nii.gz\"\n",
    "        },\n",
    "        {\n",
    "            \"image\": \"imagesTr/img0040.nii.gz\",\n",
    "            \"label\": \"labelsTr/label0040.nii.gz\"\n",
    "        }\n",
    "    ]\n",
    "}\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading dataset: 100%|██████████| 24/24 [10:06<00:00, 25.26s/it]\n",
      "Loading dataset: 100%|██████████| 6/6 [00:41<00:00,  6.94s/it]\n"
     ]
    }
   ],
   "source": [
    "data_dir = \"/dataset/\"\n",
    "split_JSON = \"dataset_0.json\"\n",
    "datasets = data_dir + split_JSON\n",
    "datalist = load_decathlon_datalist(datasets, True, \"training\")\n",
    "val_files = load_decathlon_datalist(datasets, True, \"validation\")\n",
    "train_ds = CacheDataset(\n",
    "    data=datalist,\n",
    "    transform=train_transforms,\n",
    "    cache_num=24,\n",
    "    cache_rate=1.0,\n",
    "    num_workers=8,\n",
    ")\n",
    "train_loader = DataLoader(\n",
    "    train_ds, batch_size=1, shuffle=True, num_workers=8, pin_memory=True\n",
    ")\n",
    "val_ds = CacheDataset(\n",
    "    data=val_files, transform=val_transforms, cache_num=6, cache_rate=1.0, num_workers=4\n",
    ")\n",
    "val_loader = DataLoader(\n",
    "    val_ds, batch_size=1, shuffle=False, num_workers=4, pin_memory=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check data shape and visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image shape: torch.Size([1, 314, 214, 234]), label shape: torch.Size([1, 314, 214, 234])\n"
     ]
    },
    {
     "data": {
      "image/png": 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CsCwL1WoV6+vr0snq9/tot9sTnRhFURRFURTlxkOFyvOwtLSEN7/5zSgUCvjDP/zDa/qdUCiEZ599dofP7NoIhUIIh8M4evQolpaW0G63USgU0Gw2ZXTLm8gVDAbR7/cRiUSkM+G6LgKBAFzXFU8KuyRkPB5PdFYMw5DoYj63z+eTror3+9FoVGKcKXgMw8BgMIDruhiNRiJ+KD74PMCWiGG3ZWZmBgcOHIBhGCgUCjh37pw8noEAiqIoiqIoyo2FCpWrsCwLH/rQh3DixAn85m/+JtbX16/p92ZnZ7GysrKzJ3cNhMNhHDlyBAcOHEAgEECxWES9XsdoNBK/CDsnoVBIkrocx0EkEpkQFTTAe7sTrutKR8XrVaFnxDseRvP9eDyG67rw+XwysuXz+SZiiylouLdlNBpN+GB4XJ/PJ2KmUqkgGAzKKFg0GoXP58Ndd90F0zSRSCTwxBNP4Omnnwaw1W1hgpmiKIqiKIqyv1Gh4sHv9+NHfuRHcO+99+JP//RP8bnPfe6afs8wDFy6dGmHz+6lzyEUCuH++++HbduoVCpwHGdCZAQCAQwGAxmn4vfYcaAACIVCssjR2xEBMDHWxVGvq8/DK3YoYLrdLjKZzMT58Bg8F46HsYNDMcPjeLs33NfC1zQej9FoNABAuimZTAa2bePNb34zYrEYnnnmGZw5cwa9Xg/9fl/jkBVFURRFUfYxKlQ8HDlyBO94xzvwuc99Dr/zO79zzb+XSqVQqVR28MxeHG6S/67v+i74fD6srq7CcRwRJeyc0BwfDAYRDocxHA5hmqYIFZ/PB9d10e12EYlE0Ov1EAqFJvwm7JJ4U8H4M4qW0WgkxxsMBgiHwxP7Z+h5oaDh7/PxgUAAPp8Pfr8fw+FQRBhwRUxRyBiGgX6/L4KKXZvRaITNzU0x709NTWFmZga33norhsMhvvKVr0gsM8fMFEVRFEVRlP2DCpXLBAIB/NN/+k/hui6++MUvol6vX9PvGYaBe+65B5///Od3+Axf+Ph33XUXbrnlFtRqNaytraHX68Hv9z8nrYuGeNd1xQ9CAUCRwMf7/X60221Eo1GJFqYQYFHvHdNiolcwGIRpmuj1ejLaZZomTNPEeDwWETQYDODz+Sae15scdnXHhsZ/nvt4PEY0Gp0w33tFDgAJBuh2uyiVSigUCohEIkin07jvvvsQCoXQbDbxxBNP4MKFC+KJURRFURRFUfYeFSqX+ef//J9jZmYGv/Ebv4GHH374mn/PdV18/etf38Eze3Huu+8+LCwsYG1tDY1GQwQCMLnXxBsh7BULwWBQuhvhcFge0263EQwG4fP54DiOjF5xCz09L3w8DfA06lOADIdD6a70+33p5FDUeIUKYdeGr4GdIK8pnl4THoPJYIxTZpeFx+Fr7Ha7qFQqKJfL8Pl8yOfzeOCBB3DHHXfgqaeewpkzZ1SsKIqiKIqi7ANUqAB47Wtfi3e+8534tV/7NTz00EMv+/evtfuy3bzjHe9ALBYTkQJs+TzYdeDI1nA4RDgclkKe41MAJPWLwsM0TbTbbbiuK92QdrstI2Acu6I48Pl80v0g7OR4x7H8fj8Gg8FEt4XjXvw5U8R6vR5M08RwOJQuifexPL53dAyAGPUpVGzbFiHFx3AUrNvtitG/VqshGo3iNa95DV73utfhqaeewqOPPro7b6KiKIqiKIryvFyXUDEMYwVAE8AIwNB13dcahpEG8HEAywBWAPyw67rV6zvNneVf/+t/jW9+85t4/PHHX9bddMMwcPToUUmV2k0efPBBWJaFc+fOodFoSNFtWZYU8V7BAGx1Ujg2xT0mFAz0rvB3+TULfcuyZP/J1ZHDNNBT9FDMMOKYyV6dTgf9fl9Gxtj5YZIXBctoNJoQON5xM74mJoV5d7rw+N5RN3aVvOEBfE3s/AyHQzSbTZTLZUQiEUxPT+MDH/gAKpUKPvnJT+7gu6goiqJsBzdLPaIoyiS+l37IS/IW13Xvcl33tZe//gUAX3Rd9wiAL17+et/yB3/wB/D5fPhP/+k/vWzB4brunoiUd7zjHZibm8Pq6qqYyuk18RrP2aFgoc/Cn+NQVxfxfE18PgoO7/Ow80JDvnf8iyNbvV5vwlxvGIb4VOgZ4X4UPj+wJViCwSD8fj/6/f5E14TdEq8oAiAjXnwdNOozUYw+F4oar8+Fz8vXNxqNUK/Xce7cOTz++OPodrt4z3veg7e85S07+n4qiqIo28INXY8oivJctkOoXM27APz+5b//PoC/swPH2Bbe/e5347u/+7vxi7/4i/jmN7/5sn7XMAzE4/EdOrMX5rbbbsPc3BxOnz6NSqUiBXs0GpVRL7/fL50PRhFT0FBEcLSLhTw7FPSYUJR495YAVwz5TNbi9yluvF0Qr9GdjzFNE+FweOL32AWhOKHI4eJKr1hiR8g75uXtgg0GA9n/wvPodDoAJgUZvSxeQdXr9TAYDNDr9VAoFPDMM8/g9OnTSCaT+Jmf+Rncd999O/W2KoqiKNvPDVOPKIry/FyvUHEB/E/DMB4zDOP9l7+Xd12XWxI3AOSf7xcNw3i/YRiPGoaxZ2aAX/3VX8VHPvIRnD59+mX/rmEYmJ2d3YGzemHm5+dx1113YWVlBe12W4zpwBUBwYKeCVb0cwBXRAE7ILZtwzAMtFotAJCOBxO6SCgUmngOACIOuPuEnY1AIIBOpyPdD/6ud7EjRQ6FAgURhQSFA18DOyX0qIRCIfj9fkk343nTpO/1tNBnQ+HG1xEIBBAKhRAMBkXMBQIBuXYUOd1uF+fPn8d3vvMdzM3N4Ud/9EeRTCZ35g1WFEVRXinbUo8M0NuNc1UU5Rq5XjP9G1zXXTUMYwrA5w3DmJiDcl3XNQzjeU0frut+FMBHAeCFHrOT/Lf/9t+wvr6OT37ykyiXyy/798fjMZ599tkdOLMX5vu+7/uwsrKCSqUifhMW9yzaveNW7D5495z4/X60Wi3pRLDgv3rzu7cTY1mWiBtvB4TFPrszNMRzNMy2bREm3s31pmmKmKGQGY1GMrLm8/kmRrqYKMbfD4VCEpvMDhDPlx2dQCAgSyG9z0ExwuPwd57P18N9Mf1+H4VCAf1+H8vLy3jPe96D1dVVfPazn92Nt11RFEV5abalHokbaY19VJR9xHV1VFzXXb38zwKAPwVwH4BNwzBmAODyPwvXe5LbzY//+I/j3nvvxc/93M/h3LlzLzuOlgsWd2uzuWEY+Kmf+ilsbm6i1Wqh1+vJHhTvY/iHxTaFyHg8lt+jIAgEAvI8NLAzKrjdbsM0TTG0W5Y14etwXVeESCwWk64KOyXsXtAgH4lEJq4dRUQoFJKxMsMwEIvFJLEL2BpnYyeF19o0TREiPB/ubrFtG+FwGJFIRI7Jf/b7ffk9jrwBkG4Pz8ebQEZR5ff70el00Gw2cfr0aZw6dQo+nw9/7+/9PSwvL0/EQCuKoii7z41ajyiK8uK8YqFiGIZlGEaMfwfwDgBPAPgUgJ+4/LCfAPBn13uS280v/dIv4d/9u3+Hc+fOyZ36l0MwGMRtt922A2f2XHw+H773e78XlmXh/PnzqNfrIihYIDPCl0U3uwf8mjtSAEx0EMLhMHq9nnQmHMd5jmeFKWIUQF7vCLAlWigSeC6j0Qi9Xk86Nd1uF61WSzongUAAlmVJByUcDqPT6ch7EY1GEY/HZaSL5n8AE3HLFDGmaQKARCBTDIVCITHyE4oVihE+Z6/XE3Hi7R7xWrJr1Ol0sLGxgWq1iosXL+INb3gDXv/610snS1EURdldbuR6RFGUF+d6Rr/yAP70crEcAPAx13UfMgzjGwA+YRjG/wbgPIAfvv7T3D4+/vGPY2VlBZ/5zGfEZP1yGQ6HOHPmzDaf2XPx+Xw4fPgw0uk0Tp8+jeFwiFarJd0Ojm9xxOtqD4a36CZXm9u9z0FBEo1GZc+Id8kj/RtM6+LxaN7nc3Nfi3eXC4VBr9ebSCijoIhEIhOb7fkamCTm3f8SCARkfM3v9yOZTMJxnAmfDq9Ru90GAPHdsOsTCoXEG8Pf4Wsi7FABW+IJgFxPx3HQbrcxGAxg2za+//u/Hw899BB6PZ1vVhRF2WVuyHpEUZSX5hULFdd1zwK483m+Xwbwtus5qZ3iJ3/yJ3H//ffjXe96FzY3N1/xBvLRaIT19fWXfuB1EggE8LrXvQ6bm5s4d+6cFOaMEKb/A4AU2d6oX+DKdnqvGZ6xvd4N9uye+P1+2LaNfr+PYDAIx3FEOFBwsGvDhYwcm6Kw4SiZVyzxPDlyBkAM94Svzft3dnC8GIaBbrcrXRUKFcJr0e12RZDweb1pYP1+X0Sa16MTCoXk9bND5F02CVwRfM1mE47jIJvN4l3vehdOnz6Np556akIQKoqiKDvHjViPKIpybexEPPG+JJFI4B/9o3+EX//1X8eTTz75ika+gK1CPxaLSZG/U4RCIbz+9a8HgAlRxX0fXiP4822Fpxjxela8O0u8+1b4PPR9dLtd6bSwgOdrB7Y6JvxdChYW+cFgUJ6LIoPCKhAIyJgaz4ejaRQtXjN7MBhEPB5HKpVCLBaDbduwLAvRaBSWZcmxvM/BbspgMECn05FIZJ67t2vjvX4UYd7ryK8pbPh30zRFkI3HY3S7XZRKJTSbTWSzWdx///17El2tKIqiKIpyM3G9qV83DL/xG7+BlZUVfOITn5gwob9cvJ2InSIYDOLw4cOYnp7G6uoqGo3GRLeBBT6LbgoXb8eDgoCFOItqb7djMBhIEc5FjPynV9SwK0JRwHEtpmKx0Of36Ilhp4adFfo9TNPEcDiUnS4030ej0YllkoZhIJlMIhQKScqZYRjo9/tot9vo9/syYpZOpycCATqdzsQYHLsq3q319PlQSFEE8jXyub2P5w4YbyeGIqtYLCIQCCCTyeDee+/Ft771LVQqlR39rCiKoiiKotysvCqECreL//iP/zgKhesL/aA5fCcJh8O4/fbb4TgOisXiRJcAgIw1hcPhiQ3zFDPsFlGEeLfNexckBoNBKfpphvcmgVHUcMSMG+NZmF89NsXinlHBV4sAGuLj8bikexmGgWw2i9FoJOli7Hb0+33ZaE+TvGVZGI/HaLfbcBwH3W5X/Cd8rYZhoNFooFwuSzoaN9D3ej0Zm/OOx1FUeel0OpJQ5hVKPBdCgz4FXr/fRzqdxvHjx7GysoJCoXBd4lhRFEVRFOXVyE0vVHK5HD74wQ/iox/9KL761a++Yl+Kl+14jhciEongjjvugGEYKJfL6HQ6IkS8fhSeB+/+0yvijRD2GsQ5xsRC2mtY50Z4bxeF/gymanlN4uzoeH0xNLlT5LDr4h29SiaTiEQiiMViME1TCvtkMolutwvbtsVsTw8JuyLcUB8MBuH3+xGJRGCaJvr9PmzbRrPZBAB5XbZty/hVt9tFu91Go9GQ5+ZrZWfKe334d3ZUwuHwxLgaxY53DI++nfF4jFqthl6vh2w2i1tuuQWxWAwXL16UxZqKoiiKoijKS3NTCxW/34+f/dmfRbvdxkc/+tEdFRjbRTwex+23347Tp0+j3W5PpHoBkNEuALLtnXfzvTG+3oQuxvTSIO6NMOZGdm+CV6/Xe04scTAYlO4Ij331IkkKABb3yWQSsVgMoVBIkr1isRjG4zHC4TAcx5mID+ZeFQAT/ho+t1dMsEPD53VdV5ZecvwrkUhMRDj3+300m024rgvHcVCr1dButyc6JcAVLw5H2Xid+PnxdogoHgeDAcLhsLw3vV4PpVIJs7OzOHDgAKLRqCzrVBRFURRFUV6am1ao+P1+vP3tb8f73vc+fOhDH7ohCkTLsnDkyBFZ0MgRI++YErsW9GfwTj/w3Lv9wJXOildEeJPD2JWxbVsWPbbbbfGP9Ho9MaVT6HhjgzmW5jXkc4xrbm4OiURC4ouHwyFisRiazaaIg6uXZvI1UBjQuM/uBsfRKAgorLx7YrrdLobDoSxrpOBJJBJIp9MYDAZwHEc6OKPRCJ1OB47jTERWR6NREXXeLkwoFBLPive6er08HJcrl8vI5/NYXFxEKBTCmTNnUCwWd/RzpCiKoiiKcjNwUwoVwzAwMzODD3/4w/j4xz+Ohx56aK9P6SXx+/3I5XI4ceIENjY2ZCwpGAxK5+Lqu/vsAlA8cATMWzzzZ96RJnZl6O0YDAZS+EciESnEOWbFnSVeI703VIDpXt5FibFYTDoqnU5HxsNoPgeujJ2x+Ae23jv6ZK6OLubrpt8EgCyXtCwLkUgEtm0jFAqhWq0iEAiI2KC5np2ZWCwmhv3hcIhms4lms4lWq4VGoyGjbMDW6Bi/9vqT+v3+xPJMdnLYaeHrq1ariEQiSKVSOHHiBJ544gmUSqWd/DgpiqIoiqLc8NyUQiUcDuO9730vkskk/tk/+2d7fToviWEYSCQSWF5eRqvVQqlUks4JOxkcv/KOXIVCIdRqNTHCe0e4KGpM05xIzeLiRnpBvAKA29k5ysXRL29srzdJy5skxu3ywFYUdCaTEXFCEePtSAwGA1nyyM4Jk7+AK+NXFCahUEjM/+wEDQYDeQ3xeByWZeGWW25Bq9XC+fPnRWCxazIajUQYeZPFfD4f0uk00uk0ut0uqtWqCJbBYCBdHm9YgVcscvyO58lrxveuVqtJByebzeL222/HU089hc3Nzd38mCmKoiiKotxQ3HRCJRAI4K677sIHP/hB/Mqv/Mor3peym5imiQMHDuDYsWM4d+4cWq3WxB19r0ndu0iRYgW4YpJn0QxAzOg02XuTtvhz7hRh94RCwDtuxtExiiBvtC/FhW3b8r1MJoNEIiHjT95RtEgkgl6vJ4V7v99HOByW2F/GFXNk7epEM54/o4gpfOLxOJLJJI4ePYparSav3TAMdDodGQerVCrodrvSeer3+9KVikQiCIfDsG0bo9EIhUIBzWYTtVpNulTewAJ+tti14TVi54g/Z/ep0Wig2+1iZmYGDzzwAL70pS+hWq3uxkdMURRFURTlhuOmEiqGYWB+fh6/9mu/hk9/+tP4gz/4g70+pZfE5/Mhm83i8OHDGA6HKJVKUoB7lzmywGfngkVxJBIRMUDhwXExChuKD+DKuFU4HJ7oDIzHYyQSCTGUc9SLW9oDgQAikYhE/QaDQUSjUREZ0WgUiUQCAMT7QUKhEHq9nggN4l0KSeEFYCL+t9vtYjQaiXjpdDrSCaEoG4/H6HQ6mJ+fR6vVwqVLl6RzMhgMJOKY5zgajVCv19FoNNBsNqVrQsM9u03ZbBbpdBqRSAT1eh2VSkWuHzs57F55Xy+jnK8ObxiPx3AcB+vr65iZmcEb3vAG/PVf/zXq9fp2f6wURVEURblOjEAAMC5PeAwHwA0QynSzcVMJlVgshr/1t/4WEokEfv7nf36vT+easG0bi4uLiMViKBQKaLVaaLVasCxLFjD6fD4phOmp8I5GeQ3pXm8Jf06hwihf+igcx4FlWRMGfIogACIOKIr6/T7i8ThCoRCi0SiSyaQcI5VKwbIs1Go1iQ2uVCrIZDKwLEs2urN7EYvFJro3FC3cNh+NRjEYDFCtVtFsNuE4Dvr9vuxNYSeJyyM3NzcRiUQQiUTgOI4IvU6nI2KII22BQAC2bYvI4nNweSXDCthhmZ2dFcGysbEhnSJeawAyBkeB542Spn+IIm0wGKBSqSCbzeLBBx/E5z73OYlXVhRFURRl9zECzy2J/dkMYG6F9bj1JsbP8/9qd+wC4/0/vXOjctMIFb/fj8OHD+NDH/oQPvzhD+/16VwzqVQKc3NzGI1GaLVaUmADWyKG3RVGB3u3zTOli0UxfRIApGNCgcPC2ru/hL4N3v3n8QCg0WhgampKCvxEIoF2u425uTnxxhiGgXg8LqLFtm0RKblcDtFoFKFQSEz77Mjwtbmui3g8LslcqVRKhAbHqWZmZkRsdbtdWfLYbDZljGs8HsOyLJw+fRqtVguRSASNRgPZbBaRSERGyDqdjmyop2GfiWXA1ihcq9VCrVbDxsYGYrEYYrEYgC0xNjc3B9M0cerUKQBAu91GNBpFrVYDcGWHCwDxqnD8jGKSIsdxHPR6PeTzefzgD/4g/viP/3iiK6MoiqIoyg7i80986Z+bATwhPldjJGLwJ2LP/cFgiOH683hOVbxsCzeFUDEMA8eOHcOv/Mqv4KGHHsKf/dmf7fUpXROmaSKVSiEajcJxHFSrVUm7YsKWaZoThTu/zw5AOp2WIrjRaMhiRO9SRhbNrutKl6bVasG2bXmM17Q+Ho9h2zYSiQQOHDiA8XiMVCol3YxWq4VoNIqDBw/izW9+Mw4fPgzLshAMBrG+vo5KpYLBYIAnn3xSxrJ8Ph9s2xZ/B8327NpwjIt+HIobjrBdHbc8GAwwGAzQbrfF/O44DtbW1uC6Ls6dO4fZ2VmYpol0Oi3xyIw59o6aeZPA8vk8BoMBMpkM1tfXUa/XEY/HZcFkJBKRlLBSqSThAslkEvV6fcLfMxgMZIyM508xyT801L/zne/Epz/9ad1gryiKoig7jWEgsDi3Pc8VDDz3uVwXwwuXJr5WXhk3hVBZWFjAT/3UTyEcDuMf/+N/vNenc83Mzs7illtuAQDU63XxcXhHkWgub7fbiMfjUmDTS1Gr1SaKbgoZGt29o2Isnhml+3ypXPF4HD6fDzMzM0gmk8hkMuj1eiIyotEolpaW8OCDD+Lw4cPPeU0HDhzA8vIyGo0GisUiKpUKXNeVrks4HEav1xORxj0t9MIwvpj7Szge5k3oouk+FArBsizk83kAWx0OigPLslCv17GysoKVlRWJYGaHJxQKyV4Vr1+HXpxoNIrZ2Vlsbm5idXVVOlWmaWJ+fh61Wk28JeFwWIQTTfc8D4otPrd3cSfFS7FYRC6Xw4/8yI/gYx/72A0RAKEoiqIoNySGgcDSwq4eY3j+ooqVV8gNL1QMw8ChQ4fwPd/zPfgH/+Af7PXpvCyi0Sii0Sjq9TouXbokpniasL3dhWq1inK5LFHE7LCwCB4MBvD7/eIBYfIUOyvsXDiOIwsZKXKi0aiMLS0sLCAejyMej6PT6aBYLGIwGMC2bczOzuKHfuiHMDs7+4KvqdPp4PTp0/j2t78NYGuJZSwWg9/vRzKZxGAwQCKRQL/fl8StcDg8kXDW6/Xk9bDTwmhlFvv1eh3RaFRECVPR4vE4RqMR4vE4TNNEvV5Hu93G5uYmNjc3cenSJWxsbKDdbgMA0uk0crkcUqkUstnsREhAMplENBoV/9Da2hpM00Q2m0UoFEI4HMajjz4q58dxNnavAMjoHAB5L7wCkq+THpsf//Efx+///u8/x4ivKIqiKMorwwgE4J9/4dplp3kpYTQulDC+XJcok9zwQuVNb3oTfvmXfxmf+tSn8PDDD2/783N/iXdj+XZw8OBBHDlyRLwT7KbEYjE4jgMAcBwHwWAQtm0jHo/L2BYAGRFiJ4KJW/V6fWIxIceaOFJmWZakhTWbTRnZMk1T9p9YloV+v49oNCppY69//evxute9TvacsCAnw+EQFy9exNe//nU59vT0tOxwobdmPB6j1WqJt4Ziqt1uT4ymES6X9I6nsTtBQRaLxVCv18WLQpN7IBBALBbD/Pw8FhcX0W630e/3sbq6ikKhgAsXLmBtbQ3nz5/H2toaYrEY5ubmMD09jVwuB9M00el0RLy5rot6vY5ut4tIJIJcLoejR4/izJkzkrjGMAF2VxgSwC4JQwD42eLoWLvdxtraGiKRCL7ne77nhlhSqiiKoij7GSMchn8mv9en8ZL4prLwAXArNYwajb0+nX3FDS1U7rrrLrz//e9HrVbDL/7iL+7IMegL2W44CtXtdlEoFGSZo2EY4negv8RxHGQyGYnqtW0b5XIZ4XBYno+JUizegSuJWO12WxYQApDRJBq7c7kc/H4/7rjjDjHbLywsoNfrIRAI4J577sEDDzwA0zTRbrcndrV0Oh089thjeOKJJ2DbNoLBINLptIgi13UlhYwxxRRPTOdKpVIT3hO+jlAoJCNgXAg5Ho9F5DB4gNeNAQM+n0/8NH6/H91uVyKWY7EYTNPE8ePHMRwOUS6XcenSJZw8eRIrKysoFotIJpNYXl7G7OwsLMsS4XP8+HEUi0U8+eSTmJqags/nw/z8PHq9noyHcSSt2+3K6x6NRpIoxj0s/Ezx50xVe+aZZ3DLLbfgh37oh/Anf/In2/65UxRFUZSbHV80Ct9Udq9P42VjpJMIpJMAALfVwahY3NsT2gfc0ELl2LFjiMfjN1TKF7CVeJXL5eDz+VAul9FqtWSBYSAQgGVZME1ThEQoFJIuBIAJw3Wv15MuDBOsWMx7R4vojaAPhcdptVrI5/PIZrM4efIkjh8/LrtZXNfF2972Ntx+++0iTNhRAYBHH30U3/nOd9DtdmFZFjKZjKRs8Tw4ysYuBACJCfb5fDBNU7a+02DO32OXybtkkeNt7PgwKSsUCkl3ibtm+NopUtjNYWAAsBVpffz4cSwtLWF9fR0XL17EysoKHn/8cZw6dQoLCwuynDIcDkuHhoLNMAwZGTtz5gxM05QdL0xW4+v0LoDk+BdFCwWpaZpYWVnBgQMHkMvlUNT/SCmKoijKNeGPx2FcLvRvdAwrgoC1CHR7GG48T6rYq4QbVqj82I/9GH7iJ34CH//4x/HEE0/s9em8LKanpzE9PY1+vw/HccQkzkLbu9G92+0iFApJsXu1cd4wDNkfMhgMJIKXng0A8k+fzwfHcaRLxMI7lUohk8ng2WeflQK83W7jjjvuwIkTJyZGsQDgkUcewVNPPYVisQjbtjE/P49YLCbdHHpnBoOBnFur1UIwGMRgMECr1RKhQgHCjfOBQAC1Wk28Hd4uEAARMuy08HtXd2u434QpaJ1OB/F4XEQerzGPEYvFEIlEcPDgQRw7dgyPPfYYnn76aaysrKDZbGJ2dhbJZFL2yDCS2LZtDAYD5HI5DAYDXLhwQc6J50h/Dd9Tvh4KMq+Yonfn4sWLeM973oOPfOQj2/8BVBRFURTlxsAMIzAzjeH6xl6fyZ5wQwqVN7/5zXj3u9+NJ598Er/7u7+7Y8ehybvVam3r8yaTSQQCATF6d7tdEQ5cNMg77fQ7AFfG0DgWxa5HsVjEwYMHJQ2rVCpNjFwx8YuJYDyGaZpYWFiAZVniwWFEci6Xm+ikAMDnPvc5nD17Fu12G7Zti8Bhd4jJV9z3Qu8JFy+yY9BqtWAYhrxebo7v9/uyhZ4dEkYK06PiFV3sUASDQfl7JBKRbgYXN1IQeIMFgsHgc8ztFErz8/Nion/44YdRrVbR7XYxPT2NQqEgkdGj0UgeFwgEMDs7i06nIz6VbrcroQZ8rwCIkKSYpFjjOft8PuRyOaysrOAnf/In1VyvKIqiKC+CP5WCEbf3+jR2jnAIgblZDFfX9vpMdp0bTqik02m87W1vQ7lcxm/91m/taJRrLBZDNBrdVqGSz+cxMzMDn8+Her2Ofr8v2+E5/kT/Ag3ojBPmvhXbtjEej9Hr9RCNRsXnwnQtFvj0rdD/QBM6N9Nz5IvjV/Pz8+j3+ygWi1haWsLa2hqGwyHOnDmDS5cuScGeSCTEbD4ajdBsNmXkiUU5DfoUCvTT+P1+OWfbtiXhi4U496Yw4YujY4wq5nJHCiNeM294QLPZlI5Kt9udECHePSbs2tAfw2vPzfWHDx9GtVrFM888g+FwKPtU+PNCoYBLly5henoao9FIzPjNZlPG2waDgSS3cWknDfUUSf1+f0KwOI6DcrmMRqMBn8+H97znPfgf/+N/aGyxoiiKonjw53IwzDDgM150WeNNQTDwqhQrvr0+gZfLz/zMz+D+++/HX/7lX+Ls2bM7eqxQKCS+iu1ienoamUwGjuOIYZ4jSKPRCKZpPqdzQO9JKBSCbduoVqvSlWE3Atjyq3jTr/i73u3rfr8f4XAYMzMzyGQy0r0IBALineh2uxgMBjh79iy+8pWv4OTJkzh79iw2NzdhWRYWFxdhWZZ0PZhu5Y1E9vpUhsOheFcYCMBOyWg0Qq/XmxgX4y4TvmbvtWAXgn/nuBx9HsAVD894PEYwGES32xXfytWLI3lsHsu7mDESiciul1arhbW1NTSbTYTDYdi2Ddu2UavVUCwW0el0EAwGkcvlpMPEThC7KUwqozjiOTLemMf2+XzY3NyE4zhYX1+HYRhYXl6WsTFFURRFebXjz+VgRE3A77v5RQp5tbxODzdUR+Xv/t2/i+/+7u/GX/3VX+FTn/qU+BZ2ikajIXfqt4NwOIx0Og2/3y8jURztoo+BywPZAWGRzW3wfr8f9XoduVxORsFoVqe/wbtxfTQaie+FRbNpmsjn8xKd2+12US6XsbKyItG8vV4P58+fx3A4xObmJqLRKGZmZnDw4EGk0+mJ+F0W4Nz47t3OTvFCc3wqlUK/35dRMI6wsRvDTgRwxcfB94C7YLyJWQBEBAFXxuPYtaGYoz/E6wPi1xRTXDhJEcHnooCpVqsiLCj0hsMhWq0WwuEwIpEIwuEw5ubm0G630ev1JkbPOM7H94HH4Oga3y/vws52u41nn30W9913Hy5evChdMkVRFEV5tRGYmQYu/7//VSVQXsXcMLdol5aW8AM/8AN47LHH8Id/+IeysG+nmJmZQTabFdP0djA3Nyema26GZzHMu/pc2MguB8UKRQDjiXlnn2LB+3z0RniTsljIs8PCDe3tdluWIJqmKXtVuHOkVCqh0Wjg0KFDmJ2dRSKRQDQalc4LfSUsyilKuMyR3Q/vMkQAUqh7uwzsKgwGA+kKAVd2ttBnQ+8OC3+OdfFYFAYUT96ULwoFXid2jygoKIRarRZKpRJKpRL8fj+azab4T3i9xuMx8vk8fD4fGo2GnDdH42Kx2MTeFKaweXfCcMEkcMWjwg4MPwPdbhfNZhPvfOc7pXumKIqiKK86/H4gcPmPipRXBTeMUPmZn/kZJJNJfOlLX8LFixd3/HiZTAbZbHZbuzZLS0uIxWJoNpuSUEUxcvWddu+4Fr0NLKaj0ehE8c7HXI3XxM0RLy5xrFQqGA6HqNVqOHfuHHw+Hw4dOgTDMCTZqlQqYTAY4MiRIzh69KhslG+32+KvYNIXx6/Y4WGKl+u6GAwGaDabACCRw16BxS6Gt/vi3dzO1+odo2L3hR0jihrGFdOj4vWfeFO3OB7m3XMyGAzQ6/XQaDRw5swZnDx5Emtra9IZCofDGI1GqNVqWF9fR7FYxHg8hm3bCAQCcBwHnU4HgUAA+XweqVRKRB3H2vh6KZbG47F0wShMTNOUkUNe01KphHA4jAceeGDbxxEVRVEUZT8TmM4jMJ3f6qK8GhkMMVrfxGjz1bey4IYY/frpn/5p3H333fjv//2/4ytf+coNaSq2bRuJRAKDwQCVSkW2z7OLwGLWdV0ptqPRqCx9BLaKa9u2Jc2K4gW40inw7kvxPmckEpGFjNlsVja+1+t1NBoNLC4uijAyDAPr6+totVq49dZbMTc3B8uypLPjHZHi+VFseUUFRQdH1rxhARRfFDV8jewycBwOgET4eq8Xf4eFvtfMz8cBk7HHHDmr1WoTx+Q1dxwHjUYDtVoNGxsbqFarsixzYWEBs7OzeOqpp7CxsQHLslCpVDAYDJBKpWTJY6PRkGs9NTWFVqslnRueO/0qvH70t3jTv/i+ctSs2Wzi4sWLOHLkCB577DFJaVMURVGUm55w6NXdQRmP4V6uDV9t7HuhcuLECbz3ve/F5z//efzZn/3Zto5ivRjValXuym8HS0tLmJ6eRqvVkjEpL96I3HA4LPtOvKNS3t0hvMvvNcF7Fyx6Te3AVueAd/hTqRTC4TBWV1dRqVSQz+dhGIaMIBWLRbRaLczNzeHAgQNIJpNicveOaLGLw3OkIOD5AVviih4Rrx+F5839J95xKH6PAo7eDfpi/H4/er3exJZ3Gtf53M1mE8PhEJ1OB91uF47joFKpYHV1FaVSSa43rxM7Rb1eD91uV8QDALRaLSQSCViWhVgsho2NDfT7fVQqFTQaDYxGI+RyOYTDYfR6PTSbTRGmqVQKjuOg3W7L+0XoRfHGFjOAgHg7RbVaDY7j4N5778UjjzwiYldRFEVRblb8udxen8KO4dbqMCwLCL5AOd7rY1xvADvsyd7P7Huh8g//4T9EuVzGF77wBayvr+/KMbPZLABgc3P7NoHOzMwgGAzKxvSr4YiUd3s7OwU9j4qmUZzFLQUEuwMULOxAAJAt9PSXBINBNJtNSQibnp6eMJqvra2JSEkkEmJi95q++fhgMIjhcIheryedFYqecDiMarUK0zSlmwJciSBmN8U7xkWRwscxEY2vncdmAc/Hd7tdiSb2+XwolUoIBoNYXV0FAKytraFQKMg4WyqVEgO9VxB6r7PX81KtVlEsFjE9PY3NzU3xtlA8+nw+ZDIZuK4rAiIUCiGTyaDZbKLRaIiw85rrgStiJBwOi/Ci6Ln6uq2vr+Pw4cM4ceIEHn/88R33aimKoijKXmJEzZuym+LW6hg1HPi6PeCFbowPBhhvY6jTjci+Fio/8iM/ghMnTuA//+f/jFOnTu3acQ8ePIjRaCRF7vWSzWaRTCbF/0B/CrsI3t0e3mWA/X4f8XgcrVYL8Xgc5XIZ3W4X8XhcCmmvb8NbyNOczzSqRCIh3ZBWq4Xz58/DMAxMT08jnU5LJ6ZQKMCyLMzOziKTyQC40hXxjm5x+7t30SP9Kt4RMmCyu2IYBjqdzoTYAoBOpyPCgc/NMTDvckceazAYwHEcGIYhRnd6OVqtFqrVKiKRCC5evIhQKIR2u41yuSzLLq/uTtEbwk4PPTXsbrArc+DAAcRiMZTLZRlzq9VqslvlauEUi8WQzWbRbDYn9r0AkHE0Xiuvh4YCzRtA4PP54DgOisUiFhcXZfmmoiiKotyM+FOpG0+kuC7cav0lHzZqNgHXxbg7Al7lYuTF2LdC5dixY3j/+9+Pz372s/jLv/xLNBqNXTv29PT083Y9Xinz8/OIxWITRnQuM2TxThHBDgkLVxayoVBINrnzzjw7EFxIyd8FIHf5bdtGNptFLBaT9Khut4tarSa+CxbYpVIJvV4Pt956K6anpxGPx0UIsYD2pm9xDIsdET6W6WUUZBRg7FD0+30xpkciEayurmJ1dRX9fh/NZhPpdBrRaBSDwQDRaFS6Su12G61Wa2KcC4AklPF1sxNmWRaKxSLi8biMrXGTPPe/0NsSDAYRDoele8Tr2m635Zp2u11Uq9WJ94DR0bVaDZVKRXbTUHSFw2GkUik0Gg0Z5wMg15BCiB4fCljgSlfNK1gYFx0KhXDw4EF5TxVFURTlZsKfTMBIxPb6NJ6fwRBu+wW8ou4Yo12sWW929q1Q+eAHP4hCoYBPfvKT2zqCdS1cPQZ0veRyOQSDQVSr1YnxH68Hxu/3S4HPop/LCpkoRY9Ht9sVnwbjallgX20mj0QiyGazCIfDqNVqKJfL4u+Ynp5GMpkUI3ipVEI2m8WRI0dEhPCuP8UGi29+jz4Yr4Ger4uLHukD8fl86HQ6WF9fx8bGBhqNBoLBIE6dOoVarYbxeIxWqyXFPgUSi/hGo4FGo4FOpzMh+rgkMx6Pw7Is9Ho92LYtQoh/Z5qWZVkiDrkAk10cjt5FIhGUSiVJHhsOh6jX67L80RsnzbG9jY0NDIdDLC8vT6SRBYNBxGIx2LYtAob7YSiYuMXe67NhF4nnys9Ou93G5uYmDh48iPX1dRUqiqIoyk2HkUzsynHc5sv3e7rdHsaXbxIrO8u+FCpvectb8Na3vhX/5J/8k13zpXgpFovPG/f7SojFYpiamoLruqhUKjISxBEv746TUCgkxnAA4lNgjC9N6SySGXHL7gy/R08HI3UtyxJBwe5HNptFNpuVopgeinw+j3Q6LeNL3nhdGtopigaDgSxD5Gvw+lj4daVSQa1WQ7vdRqFQwNNPP41+v49qtSpdJHadwuEwTNMUDwxFAoUO97yMRiOEw2HpwPh8PiQSCfR6PcTjcREFoVAI8XgcjUYDlmXBNE05f4578XumaUqgAEeqeL25xZ7nEQ6HRUCxA9ZsNuXaRiIR6SqFQiFEIhHEYrGJETfgytgZxRw7azyut5PF5ZDj8RiNRgPtdhvZbBblclm6aoqiKIqiXDujcmWvT0F5EfadUJmfn8e/+lf/Cp/+9Kfx9a9/fddjWJnqtF2F39LSEuLxuHRCOFLGYpQwhpZjQ14zPIthAPIYFtq8K8+UKgog72N5bHZikskkpqenpUNTq9XQ7XZh2zamp6cxHo8RiUSkWOcYWTAYlG4JxdHVCydt2xYxViqV4DgOTp48ifX1dTQaDVSrVVSrVdnnks/nMTMzg6mpKViWJaIgGAzKa/amYA2HQ7Tb7Qm/BjslHB3juJTXhM5zD4VCcm0pULh0kcEBAMTHwveMe1DomfGOaHnjpbvdLjY3N7G0tIRgMCgjawAQj8fhOA5arZYIRuDKGBi7OhRDVwvSq1PoeJxut4vTp0/r1npFURTlpsC3U8uNXRdu99UZ83ujsu+Eygc+8AGMx2P85m/+Jsrl8q4f/9ChQ3AcZ9vGzbi5nGZqGuW9Ra5350g4HJ4wXPMOu/fuPdPDOBYWDAYlIYxFLrC1uyUWi2E4HKJcLsNxHJimKcKA40Tr6+vw+Xw4fPgw4vE4er2eLKYMBAJotVqyd4UFM4t1CiaOqfX7fRkxe/rpp7G2tobV1VU4jiNjVfF4HPl8HlNTUzh69CgsyxI/ijf2mK/Few0Mw5Au02g0EsM/uyvAlleHnpjBYIBGoyGigIKFI2oUXKZpIplMwufzyUid4zhIJBJirufIViwWk6hkx3FEYPl8PnS7XQkkmJqagmEYskMlHA7Dtm2JTva+TnZT+PdQKCRjYV7PCt8DwzBkweTCwgLq9fq2hT8oiqIoyl5hhMPwTU9tz5O5LtDre750MdosbM9zK7vCvhIq2WwW73vf+/DBD35w1/alXE0qldq25+JyxfF4jHK5LN0Uxviy8AWuFKv8uddkzzvqwNbddyZascDm8/LOf7/fl50f0WhU0sZCodDEmNNoNEKj0ZDxqcXFRRFLNOqzY8OuBD0x9MiMx2OEw2FZXLixsYEnn3wS58+fl4Qzx3GkU5TNZpFKpXD33XdjOBwil8vJ7hHLskSQsTvh9/snFl5yRMpxHMRiMdm3YlkWUqkUarUa/H6/PF+r1ZrYdM/n8SaT0WDvPTbH3ILBIFKpFDY3N2V8jQKK58XnppjrdrtYX19HNBqFZVlyTABIJBIieoCtzhT/MLo6Go2KCKXY4r6Vq/0rpVIJR48eldjk7RpZVBRFUZS9wD+T354ncl24rTZGpd2/6a1sH/tKqPzYj/0YPv7xj+N//a//dVOMsczNzcldeprRx+OxjAKx4PUu+fMKFhrFvf4E0zTRbDZFqHjvxjPOl90CCohutytpXNlsVro3XCLY6XRgmqb4TdgpACApXfw+hZHXMN9oNFAoFHDy5El85zvfQa1Wk10g3JMCbN3JsG0bhw8fRiaTQbvdlnE1JoJRnPF3HMcRvwbN8Uzx8qZvARDB4h11Y/eBHQyKPiZqhcNhRKNREV48z1arJeNpg8EAyWQStVptIp756s4Xz4VdpY2NDSwvL8O2bVkMadu2LIHktfF2TdiVIVxayZ01Xv8PxeHm5iZSqRSmp6dx6dKl7fwIK4qiKMqNw2gsyxHdXk9Fyk3AvhEqfr8fiUQC/+Jf/IttjQZ+uXCkajuYnp6Gbduo1+tot9vodDpSlNNH4h3rAba6KUz48vv94tExDENGp9idYJIV775zs7tt27AsSzoLlUoFrusimUxifn5exs86nQ4KhQKi0Sii0agUw8CWR6PX62E4HKLVaiESiYgI6vV6YvS/ePEiTp06haeffhrValVEFDtBFASj0QipVAqZTAZTU1PSTaKxnMIjGAzKhnjHcURsAJDzZGEfDAaxuLiIVqsl3RDuQqnX66jVaqjX64hGo2g2m+LJ8Qq/WCyGWCwG13VFsLHbEovFMBqNJNDB678BtuKK+V5w14y3W1Or1WQHTjQaRb1el895sVhEu90W8Urxw3AB+mnYCePYmzdtjO9jpVLB0tISpqensb6+PuF9UhRFUZSbmtF4a8QLgOs4GNVeeoeJcuOwb4TKgQMH8Cu/8it7eg6maaJYLG5L3CuXKXL3B4tQ3tX3Lhfk3X4WuleLD4qWaDQ60Wmi/4EeCo5zGYaBeDyOdDothXM8Hkcul8NwOBQhs7GxIWliuVxOOgx+vx+O48joEfeAsEszGAzwzDPP4OTJkzh58iQKhYJ4NILBIBzHgW3bUrDzNY3HYySTSZRKJczNzYkoGgwGYoRPJpNSvDNZzO/3I5PJiHhyHEdEC68Ru1SxWAybm5uoVCrynEwO4/WimEsmk8hkMhJf3Ov1pGNC0z6FZT6fx8bGhnha6HsJh8PSGeLuF74P4/EYq6urME0TmUwGw+FQ/D+5XE66Pl7R4V1E2Wg04LquXLt+vy/JcOw8UciVy2UkEgksLCxgZWXluj+/iqIoinIjMK5UNSr4JmbfCJWFhQWcOXNmYlv5bnPbbbehUCigULh+oxW9GNFoFBcuXJAuBO/8U3BQGHjN9fwZC1j6QLgfpFKpyF11RghTQIRCIUm9op+EY12JRAKWZYkJvtlsThi3uVeEI2necSTTNNFqtXD27Fl8+9vfxsrKinQFvMsrvT4W3v33el2ArdEomtG5TX48HqNSqcjIVSaTEa9OIBBAIpEQY3+325UAgdFoJCla3pAC784TdoLYrfD5fIjH4zBNE/1+X0Ib6H/hn06n8xzfSiAQQK1Wg2masmfGdV2JNebyTHpLGMwQiUTkvWUYQCy2tcjKG1kNQLxE3nhihhcw8Yznzmtfq9WwuLiI6elpnD9/fk//PVIURVGUHUX/H/eqYd8IldnZ2b0+BSwvL2M4HOLChQvX/Vy33norTNPE2toaNjY2UK9vtSJ5F54FMO/Ys/DmnXmOiXFvSSwWQ6lUgmEYsiMkEonInXav14HmbBbupmkikUhIoR4KhbC6uopSqSQdgHa7LZHJPC4Tt3q9HjY2NvDNb34Tp0+fRrFYFK9EOByWTgXFCAvtRqOBWCyGfr8Px3Ek+jiTyYjXxrZtMcezYxSLxRAMBmU8i+IpEAggn8/LqJfP55PdJN1uFxsbG9IVqVarzynWq9UqEomExBkbhiGdmMFgINec5vpkMimja8ViUbbQt9tt8bbQ35LP52FZFuLxuHTBer0e1tfX0el0UC6XkclkEI1GsbGxIT4erz+JaWPD4VAS2jqdDvx+vyyZ5PVl14fvD0XZ4uIiut0u/uZv/ua6P8OKoiiKsm/w/D99tLYJd3Dje5mVl2bfCJVjx45JstFe4fUnXC+JRALJZBKVSkWKfnYQHMeRTgbvyHPci4sMeR5Xm+25jZ3dCRrMvWNWTIbiSJTruojFYrIwEdjqdqyuriIajaLRaMBxHNTrdRErFD8rKys4f/48vvOd76BSqUg3AIAkblEY2bYtz81IXgBSXNNPsbCwgGg0CsdxZEFjKpVCvV6XTgMFB68LfTaWZYmg4SLJSqWCer2Oer0uCxUptujVoSBpt9vI5XKIx+MSH9zpdNBoNKRzlcvlEI1GRbz1+33U6/WJ6GemdPl8PuTzeSwuLiKdTovgHA6HSCQSSCQSKBQKIgCZuOb3+8W/RHHCUTt2vLgbx5v45k2Ao1hjJ6bZbGJ+fh5zc3MqVBRFUZSbiuGFS9pJeRWyb4TK0aNHpbDdK7zG7evF6z1hjK9hGFKU8mtCnwM7JF5zPI3m3sWRXNA4HA4lijcUCsmWdsMw0Gg00O12kUwmRQT2ej24rivLGBOJBEKhkCRH9ft9lEolAMDp06dx5swZjMdj2f1B8zg7QvzDES+fzyeRyNx5AgCWZUnhXyqVYNu2HJ+LKSuVCqampmQzPTsTfG0cd6pWq7KrhaNQjUYDuVxOQgMsy0Kz2cT09DQqlYqMiwWDQczNzSGfzyOVSomI5GgYsCXAKE54zQDIeFsymRR/zdTUFG699VbMzs7KNfWmpEUiEczNzUnniolmo9EIzWYTsVhMRJTXMM+Fm+wkAVdGwgBMfEY4ggYAxWIRg8EA2WxW3kdFURRFuZEZrlz/pItyY7JvhAoLsL1kZWVlW5ZMGoaBVCqFZrOJcrks3gd2VNhdoVeDnRXgirE8EAiIL8QbewtAhIDjOEgmk2g2mxIpzCWPfH7vBvROpwPDMFCv13H69GkROfSmMLHrO9/5DqLRKM6fP49AICD+i16vJ+Z77/uVSqWQzWaRTqeRzWbh9/tRr9fhOA7Onj0rPpBYLIbz588jFotJh2gwGCCdTsuYGL02lmXBtm1ZbMhuQrvdRqPRkCWUsVhMTPb031Ck8PVzdI5mf5rTKUQASDIXr3Gn05Frx2vJnTNTU1uLqHK5HG6//XYJKWAAAHfVcEzMK7aALR9LOp2WRDfue6EBn34k7sOhZ8Z1XbkWDE3wdnkokmZnZ3HXXXfhC1/4wnV/lhVFURRlNxmuXEBgeXHi62vBd+dxNI4mXvjnAxfRP33kus9P2V1eUqgYhvE7AL4fQMF13dsufy8N4OMAlgGsAPhh13WrxtYt6X8P4J0A2gB+0nXdb17LifCu9V5x5513olQqbYuR/u6770YqlZKimssBvclY7EIAmPg7d5+wOE4mk9KRoe8jEAjICJfXRM7liPF4XNKo+JwAxANTrVYxGAxkR8ilS5dgmqYkVDWbTaytrSEej4v/w7Zt8YsEg0FMTU1hfn4eBw8exLFjxzA3N4dYLCadEo5MnT59GidPnsSf/MmfIBAIoFqtSgcFgIyaRSIRiUemiKEPpNVqiXEe2Er2ymQyEihQr9eRyWTk54wjtm0b/X4f09PTKJVK0qmhp4NikKNiHLNjl4gxy47jiBCJx+OSyDY/Pw/btlEsFpFKpRAIBCQNzLs4k2lp7KaEw2F57n6/j3Q6LVHQ3KMyHo9hWZZ0VhiSwDFAClyKH14vjrJxl85eRn0riqJsJ7tVjyh7z8vpoLgP3InWvAm8xFTOOGig/YPfhej/ULFyI3EtHZXfA/BbAP6r53u/AOCLruv+G8MwfuHy1/8HgO8DcOTyn+8C8JHL/3xJWJzuFYcPH8a3v/3tbRFMs7OzyGaz0k3wJmgBEAO967pSfAKYGDuLx+PY3NwUszTHgrj93Ps8FDWJRAKpVArxeFzM4dyUzphhANI5oaAAIAW/ZVkwTVOKfYoVGrd5t/51r3sdlpeXYVkWgsGg3NWnAT4SicB1XRw7dgy33XYbZmdn8dd//df42te+hrNnz+LgwYMSBQxsiQuOtXEcqlqtymsGIAU/96EwNSsYDMqo22AwwObmpuw8oT9lPB6L6KEgYkcJ2BLKFIEUTUzu4nvF6x6NRjEzM4PFxUU4jiMjaEz0Go1GE52ceDwuUcJ8T8LhsIye8bVwfw87JgxL4GeHo2sUKeyE8TrysxYIBDA3N4e3v/3t+OxnP3vdn2dFUZR9wu9hF+oRZf8zeMdr0Y9vTYu4vmu3DYwDBlo/9F2w/kTFyo3CSwoV13X/2jCM5au+/S4Ab778998H8P/D1n8Y3gXgv7pbFd3DhmEkDcOYcV13/aWOs9dCZW5uDmfOnNmW5xqNRrLg0Rs9TLzpWBwHoxDgmFi73UY2m0Wv13vOXXHDMCTG17uAMJ1OI51OS1ek1WpJEtXtt9+OEydO4JFHHkG5XEan00E4HJZ0KQCy64XjTxxhmpmZQT6fx6233or7778f2WxWNsDTQ+NNDPNuomcs83333YdQKIRSqYRKpYJnn30WBw4cELEWiURkNI37S9iNYqeEI2zea2gYBqamprCxsYHBYCCeDgqTwWCAcrkso3HVahVTU1Ny3ehdYRhAJBKR/Sz0kTQaDfHcUPjNzs4iEAigUqlIZ8QwDNke3263YZomksmkpKp5965wn02tVkOj0UA0GkU8HpeODHAllODqzw49KfS8cJcO8fqiFEVRbhZ2qx5R9i+9d96LkenDOPDKPc2uf6uzAkC7KzcAr9Sjkvf8y74BIH/573MALnoed+ny957zHwbDMN4P4P38eq+LKq+H4HrhfhPuFWl5FhFx/IpRwTRd8244R8QAyJJHAFKwsjinz8W7AJCCY3NzE8ViUba333nnnXjve9+LSqWCr371qyJEeB6RSGRiNwm9IYuLi8hms7jzzjtx7NgxJBIJxONx6WxwHItLCtnhGQ6HaDQaME1TRtRisRiOHTuGt7zlLfiLv/gLnDlzBouLizJq5TiOeGzY9eD1oZCgaAmHw/D5fJKKRWHAMSt2GZicxWWO3I3iXShJI73ruohEItKNWl9fly4FC39GJ+fzeUxNTSEej8t5UyzydVPkFItFOXfbtjEYDFCr1aRzk0gkZEyrWq1KJDTH+ShmHceZMM3zfWcABDszvObBYBC9Xg+33XYbnnjiiW35XCuKouxDtrUeMRHduTNVXjHd778PbgAYhXzb8nzXI3SU3eW6zfSu67qGYbzsvDjXdT8K4KMAYBiG6924vhdsZ+IXjdSO44i/gCNWLOq9RXIgEJgQNO12G/F4XMQLAInLZWHPJY8UCjMzM8hmszBNU9KnwuEw7rjjDtx3332SmFUqlWBZFqrVKoCtrkkwGBRPB9OzDhw4gDe84Q2SwpXNZic8HSziORbW7XZlxIrjWqlUSsa4bNtGLpfD/fffj7W1NTz88MN4+umncfz4cUSjUekkcYyMz0FBFg6HEQ6HxYAObIk+dpVoQKf/o1KpIJFIoNPpwDRN6bbQkJ5IJBAMBhEKhcTzMRqNUK/X0e125dpTNPR6PdmvcujQIeRyOSQSCdke3263ZVdOOByWLke73RbDP7C1iHJ9fR2RSEQ8LhReTGnj+0wvEk3/gUBAYp9DoZDEWLPjRpHW6XSwubmJaDSKI0eOqFBRFOVVwXbUI3Ejrfm3+4ze37oXw+j2CJSr6f7t+2D++dd35LmV7eGVCpVNtlANw5gBQAf6KoAFz+PmL3/vJdnr1K8LFy6g0Whc9/N4l/7V63W5s8/OAAtydkHYiYhGo+K9YCoVd43Q4+Id+eFuFIqGXC4nyxMpHlKpFG655RYcPnwYwFanp1KpoN/vy9gXR7coBGzbxi233IJ3vOMdWFpagmmacBxHRqFoPOfv0ktCjwYFmHf/Bwt9y7KQSqXwpje9CU8++SQ2NzextLSERCIhIQJM/er1etK54U6U8Xgsfh2KDI5CJRIJ2ULPpCwmgOVyOQkQAK7sy+HxIpGIHMO7gLPT6aDdbkuHhwsm8/k8XNeV2ONKpQLgSrywN3qaXROea6fTQavVQqfTmYg5DgaDWF5eRr1eR7vdlgADLoH0iil2aBhlzCAACjGms3GTvaIoyk3Mttcjyv6g9857AQADy/8Sj3zlDCM7I4CU7eOVvkOfAvATl//+EwD+zPP9/4exxf0A6tc6D7qXO1QOHjyI8+fPS8F5PRw4cADpdFrGmXq9nhSVLJ5Z9HLXSrfbFTHAwpUjVMCVxX7D4RC1Wm1i3IdRyNyiXq1W0Wq14LquGL75nMPhUHwgjLflkkGKp0OHDuGBBx7AsWPHYFnWxJ18x3HQarXQbDZRKpVQrVZlpwl9FxQxfHypVEK/35cRKtd1ceDAASwvL6PT6YioYjeCBTm7KxzjomhgYhdjhmOxGEzThG3bSCaT0uWo1Wq4dOkS1tbWcP78+QlTPv1D/X5f/k7TOsUWrxETuCiGcrkcwuGwdEbYOeE+Fo5c8b0xTVMWcpqmiXw+j3Q6LcflaB+ff2pqSrop/X5fRgh57oyG9gYw8HrxfeSYGwXPnXfeed2fa0VRlH3Kttcjyv5gYPsxsHdOpCg3BtcST/xH2DKqZQ3DuATgXwD4NwA+YRjG/wbgPIAfvvzwv8BWFOBpbMUB/tS1nshexqgeOnQIp06dkmL5esjn87AsC8ViEQCkAPb6UFgIe7fO8w+LVI58UdS0223xkjDOeDQaodVqYXp6GplMBvV6Hc1mU5ZH3nPPPbjjjjvk3CieXNdFrVaTLsRgMJA0rbe85S244447pBA2DAN+vx+VSgWVSgWRSAT9fh/NZlP8IeyiUATxdQ8GA7RaLbnzb9s2fD4fkskkbrvtNpw/f14eyySsbrcrPhQAsmvGMIyJbgVFnGVZstslHo9jbW0NtVpNRt+GwyE2NzdlJ4lt21hdXUW73cb09DRisRjq9bq8Fyzuvf4b4EoXhrHCXOCYzWYlJGAwGMgSRo5kOY6DaDQqBn3bthGPx7G6uirPy+OEw2FMT09jbW1NBBwjiykq+Vh6VtiZo6CjYOH7PRqNcODAAXz729++7s+2oijKXrJb9YiyhxgGRm++G+4u3rwevvUeBP7qm7r1fp9yLalff+8FfvS253msC+BD13tSu83s7CxOnTol6VLXA6NtvQW8F4oW7xZyb3fF5/OJnyQUCknBzUI+FApJjDAfQ68C/RXBYBC5XA4LCwuIx+MTx2YBblmWFL8AsLi4iKWlJdx5551igGehOxqNUKvVRKiMRiPpkPCcAcj14+vj41hAe1/74cOHEYvF0Gw25bWx2B4MBiIEGCVMrwqfn4swQ6GQRALXajWsr6+jWq2i0Whgbm4OyWRS/DR+v186LxcvXsRoNMLi4qKMplHYcEzMmzjm9avQdB+JRBCJRKSzU6vVZN8Kt8gPh0NJ/GK3hV6ebrcro2u1Wg2u6yKXyyGdTuPs2bOyF4XH5/lwj4s3dIGPo6DjSCFDGLhUUlEU5Ubl1VCPvOoxfOjktifY6FrpZoOwd/WIysth3wzneaNndxve7d4OYrGYFKO80331c7Oo9Mb4ch8KxQG7DCw0g8GgRPIGAgF0Oh30ej2k02nkcjn0ej0p3gOBAE6cOIGlpSU5Jr0UFEccV7IsC5lMBvfeey8efPBBpNPpifeChnD6I3hc4IqQabVaEiXMYwUCAenWMAWNosdxHMzNzWFxcVFEBaN2uZCxUqmgXC6LZ4SdBI6EMbGMgqlaraJQKEjxX6lUUCgUEAqFMDc3h6mpKcRiMfk6nU6LF4bvQ6/XQ7PZFH8HBQBjhr0xzEw/48gbk7soKOih4QJLCh7+nR0ob1IaTfSZTGbic0P/CY/PcT3ve0RRzO/zeLz299xzz3Z8vBVFURRlZ/D54X7XbXtyaPf+OwCfjpntR/aNUNlLjwpFwXZAX4d3SSILU/oM+Fqvjsnlz2iu7/V6Ewsfub2c40LBYBCzs7PIZDIy8sW76YcOHcLi4qKcV6lUmkh/oqCJRCK488478drXvhbz8/PSVfAWyYZhiBfEe/eer8lr4KcYoQjg+BP/8C5/OBzGkSNH0Ov1UCgUJq4FvRbNZhOVSkU6EPRjcGcJO03cvcLULEZd0yzPLoplWbAsC9FoFJlMBolEQkbtuI+kVqsB2BKvHBWj98XbJWHHiGNafI8YlkBhxefnMRg5zfAAx3EAQHxA3FSfSqXkmnA8kNf76k7L1d0Ub8eOxvoDBw5sy+dbURRFUXYCw2egtRDZk2O3FiIwXsbiSGX32J42wjawlx0VdiKuFxq8vXf/KTzYLbi6u+Ld6M7xoPF4LMlVAKTzwTv9LEhjsRgSiQT8fj/q9bqMNwUCAaRSKdj2lWam4zjY3NyUO/4+nw/RaBTpdBr33XcflpaWxFTO/S80rXPMiQKEe0XoxeCeEq+fot/vi7ji8ehXYWE9NzcH13VRKBRQr9clzSwSiaDdbsNxHBQKBRmJoggDrog9bo5vNBqyJT4QCEiUsM/nQ61Wk4WNuVwO4/EYsVgM4/EYpmlKPHKlUsFoNJLFj67rytJL7myJRCKIxWLSyaHnhz6fq0fher0eQqGQmPT5WWAaGEfjONLXbDYRjUaRSqVQq9VEvHF0zivoveNwXihW+JneTiGuKIqiKIqyW+wbobJX0FuwHXtcMpmMGMyJt0PAu+Ms8Gm4ZrcAwMSYEyOLK5XKhPEegBizAaBaraJer8sde26998JdJBRS0WgUyWQSS0tLOH78OCKRiJjfaYrvdDpwXRe2bcM0TUxNTYm3grG9AGSbO4CJcSia0+m1oWjhSBQjlcvlMi5duoSlpSUZ62JSGa8FxRNjnkOhEGq1mointbU1lEolDIdD2SczPT2NdruNs2fPwnVd6f6wq0IREY1Gxcwej8clNphiiyI6lUohEonIe0y/CiOA2eVi5wbYGoNrNBriueGYWCQSQSqVguM40vnh472LIH0+n/hYKMq83RR+xoh3rJDjaBSf8/PzuHTp0nV/zhVFURRFUXaDfSNU6HvYbZaWllAoFLbFaJzNZsVvQGFCocIRJeId1/HCsSHu7uAyQPo+uL3dNE2k02npSHS73YkdLhREhmFIscyuzmg0QiwWQzwex6FDh5BKpTAYDBCJRFAulyVSmGNafr9fivtQKCTnxl0jAOS4DBLguBtjdlutFtLpNADIedi2jZmZGRQKBaysrCCVSiGTych1sSxLRpharZbsWGFngYKpUqlIRyOdTuPYsWOwbRuZTAaPPPII1tbWkEwm4bou1tbWkM1mMTU1JbHBHN1ilHAkEhEvTLPZFHEUjUYRDAZRKpWQSCQwHA4Rj8dFoHA/Dd8HbpRvtVry/rHzBEC8PezgMHp4OBzK13y8YRgi1rxGeo6EAVfECTsw/DmjmF/72teqUFEURVGU58F3YBGj0+c0/Wuf8aoXKkeOHME3v/nNbREq3EDP4pR3urmkj8WmN5kJuFK4swhl16LT6UjhS3FA4ZDP55HNZqUITSaTyOVy0kXo9XpwHAeRSASrq6s4ffr0hOeBiV2JREL8JF7fRL/fh2maYib3+kG4uDIcDqPdbqPZbErXgY9JJpMyqkbxxc4DBQIXVQ4GA1SrVZw/fx6macKyLPh8PqRSqQnfB3eWUARyp0swGMT8/LwU9IlEQjpHjHNm8hejmyn2KOIYHc3XxnCCQqEgXiCfz4fBYIBGo4HxeIxGoyG7Xtjx4HvMgACKGwosr6emWq2K2KEnhos4u90u4vE4isWinKc3zYujXd7uCgUi4Vhet9tFs9nEwYMHr/szriiKoig3I/W7pxC7sAp3j+pR5fnZN2b655u13w0OHjw4kcp0PXA0yZty5fWoeI/DER7GGPNnFDUcEWq321KYcuSJXotoNIparYZwOIzl5WXMz8/L6NLm5iaeeeYZPP300/j2t7+NtbU1uQvPUaxutyuCimNo3AZPTwU3zjNil+dCwcLiOhQKIRaLIRwOIxgMIp1Oi/menRFgy1PCYns8HiOdTksBf+bMGZw5cwbNZhORSASZTAbJZFLSuigEAGBjYwPr6+sIBAI4evQohsMhWq0W6vU6isUims0mTp8+LdHNfr//OYsdvcspuQyz3+/LY6rVKsrlspj3vfHKxWIRrVYL1WpVxqsoODudDkqlEgqFAhqNBkKhEOLxuIiwdDot3ZpOp4NGowFgS9AWi0WUSiURmexisUPCawdAhIu3e0fx6/33yXVdtFotjEYjmKZ53Z9zRVEURVGU3WDfdFToSdhtuNF9O0in09ItoWihSZ537L3L+Lx7TAhTvTg+BgCVSkXGs3w+HyKRCHK5HICt4nZmZkaOTb9Dr9fDww8/jFKphHa7Dcuy0Ol0xLBP3wcLdZrHw+EwMpkMWq2WeExqtZrsIGFHgJ4Kr1+D+2Dy+TySySR6vR6SySQqlYose+RYFYttjrZRNJw6dQqGYeDgwYMiTJhORoP9cDjEpUuXEAqFkM/nUavVsLGxMeEzcl0X1WoVrutidnZWxsd43ev1uohACkvTNDEzM4Pp6WlUq1Wsrq7K++MVBIlEAs1mU8RkLBZDp9MRcVCtVmUEjl0Xvm7DMBCPx5HL5USQsNMUCASwubmJ8XiMVColxnz+k+KWgsnbqQGuJIB5z5UdLUYvHzp0CE8++eS2fN4VRVEURVF2kn0jVPYKbhrfDljUdrtd2TsCXBmHYuFIAcM9KzRNe3+fiU7srnCMxzRNiR0uFApIpVJIp9PSJQkEAjBNE4PBAPV6Ha1WS7o5jDvmOFez2ZTOAMUNF0RyBC0SiaBUKqHZbIpPBYCcNwDxWHA0qtPpSPQvH8dOER/b7XbFMM7ULO5QefbZZzEejzE3NyfvjzcOmMJpamoKq6ureOaZZ6Tbk8/n0e12JVzA5/MhmUyKJ4Y7briDJhgMitl9bm4O+XweACZS21jkZ7NZ8Y9wd00kEhGR4k1wo2fFey0BSGpaNBqVz0S73Ua73ZZrSB+S12TPzw07UQAmAhq8qWC8pt6xsG63i83NTRw5ckSFiqIoiqIoNwT7RqhwseFuw9Gr7YAdg0AgMJH+dXUsMe9y8/j8HmHRads2ut0uotEoOp0ObNtGIpGYMIJzpIiGbu+me3Y6WLRaliVdBHZ6ms3mxNgbU9AYj2xZFvr9Pvr9/kSyGJcbApA7+/S1mKaJYDCI6elpiTFm6phlWQgGg3AcB6lUCqVSSUaxuJ2eMcOtVkuuHQv3YrEoAu/MmTNwXRfJZFJGxGzbltcHbIkEelDYven1erAsC/l8XsQCF0WePHkSPp8PiUQCc3NzsG1b9r9Q0FIAcbTNG1Ncq9Wka8JzZxwxAwDYkWInxhv1nMlk5FxarZZ8jihymaLGjh33xvDzxPeHnwF+xhhqkEgktuWzriiKoij7DWPkwje8vEzbZ2Ac1N0oNzr7RqjwzvFuc3Whfj1EIhHZqu4dvfEuUGRBSXgnnt4B0zThOA7C4bBsoudSwFQqJRHBw+FwYo+K67oSuctuDUUDC3d6Hrh0kCZrjiil02lUq1V0Oh1JsxqPx0gmkyJWOp2ORBm7rivCwxsAEAgEkEwmEY1GpVifn5+Xzg5jc9fW1nDq1Ck0m015Td49K6PRCOfOncNwOMSRI0ck/pm+GtM0MT8/j5mZGViWJf4gYKuLxeseDodRr9fhOA4syxIDPonH47KLhVvnKVQ4BsauSigUEl8Kuzg05HMRI+OO2Vnz+/0Ih8OIRqOwbVuuy9GjRxGPxyUOmrtxmKDGrgu7Td5RrsFgIBHX/H3v7hTCGwD8Pj9vV38OFUVRFOVGx15x4D62NTUQmJlG7Y3L1/y7FDjK/mLfCJW9Wkh3tbfhemCBzQKShEIhuWPO9CmavL0xvt4ClMZuig1gS6jMz8/L41jwsihn14IiiXfSo9EoWq2WFPHhcBjVahW2bcuIE8+DEcPD4VAWFQ4GA9i2LeKEngp2Srhwslgsiimdo2Ycg+JjpqencenSJQQCATz66KM4deqUpHmNRiNYliXFOjfBb2xsYG1tDZZlYXl5GY1GA2tra7BtG7Ozs+LfYWIXuw7ekT4KIW6w9/l8KBQKME1TRBU9O6ZpIpVKiQBLJBLY2NhAu91GJpNBu92Wa8FrmsvlZOSO6WzeY/N6MaY4HA5jZmYGmUwGruvi7NmziEQi4pnq9Xqo1WriQeFnhftxvJHEXtHlHQGjwOFnsd/vo1Kp4PDhw3j22We35TOvKIqiKNuFMXLh+l9hF8R1gfHk18bocm1pbHVYXgzrU4/B3Ybl38r2sm+ECvdx7CaRSGRbhUq/35fitt1uS9HIu/28w8/xI5rFvR0Xr5me8cOM4mXiFxc3snhlR8VbELNLw0QrjktlMhlsbGyg1+thamoKgUAAFy5cwN133y2Run6/X7bIAxDxxNGy0WiETqcDx3FQLBYxGo3QbDZRr9fRaDSkG2BZFgaDAYrFooyLNRoNrK6u4utf/zq+/OUvS0RxtVqV/THRaBT9fh+RSEQWJ5ZKJUQiESwvL+PgwYOIx+OyRJFdI/pFgK1OQiaTkWudTCZFbPR6PTQaDbRaLSwtLSGbzQIAarWadK+8vhnGLdPTkkwmJW6ZnSsGEDDpi+8RPxdeAcH3Atgy3pumiVqtJgs8gSvjZdwdQ9FKA7/ruuj3+7Lgk0KMwoTjfnzvKZQbjQZe//rXq1BRFEVR9hXucAjrTx+F8557X9Hvx5+pY/ydp+Xr4cYmrD/ZBAD4LAuNd962Leep7C77RqjsBXfeeScuXLggHYLrxe/3y/JB746URqMhI1csVsPhsHQx2MGgWKnVagiFQlJM53I5pNPpCc8EcMVgTXHAQpfeBi6MpFmb4iYWiyGbzaLf78OyLJw9exZHjx4VkzsXDfb7fYRCIREA7MBQGAWDQSQSCZRKJZw8eRKnT59Gv98XLw3FTb/fFwP8E088gWQyiePHj2NxcVF+9sQTT+DMmTNyrEqlgtFohF6vJ16dCxcuIBQKIZPJ4MiRIyiVSjK2x25KIBBALpcTQVCr1dBoNJDP5zE7O4unn35aoqNvvfVW3HXXXcjlcigWiwC2RBk7K6PRSAz4TO5iilomk0E6nZZzpECp1Wqyq4XJXxzN4ueBCWf03Tz11FNwHAedTge5XA4XL14UI30qlUK9XselS5ckqc2yrImABUYjM2KaXRivGZ/CyhuYoCiKoiivBsatFuxPPvKij9HBr/3JvhEqezEzv7y8jIceemjbujmGYSASiUjxzFEvehe8d7298cX9fn8iBjeZTEoMr9/vR7vdxsLCgpjR2THpdDqIxWJIJpOo1WqSFsZz4cgQFwwCW2b5qakprK2tAdi6o+/z+dBqtZDJZORcGOXLu/nebg4Lfa+fIp1Ow3VduZZnz56F67rIZDIIhULIZrMiYA4ePCjGdp/Ph36/j/X1dXzxi1/EyZMncfHiRTHvU5SxCO90OhO+Il4Hdlccx5FIY45E9ft9uc79fh/ZbBZ33HEH7rzzTjl/ekoMw4Bt2+JFYdeCCWVcvslrwJhijpRx7I/+FL7H4/FYulV8LBd8NptNtNttFItFZDIZWfQ5MzMjz+XtvHnjjAGIP4ifLf5hwhv9KbZtIxaL7dnOIkVRFEXZKRrHkogFT4hHRbk52DdCZS88KsvLyzLzvx2EQiEpIr37LPgzdlCAK6+XhS13rXDkid0R3nmfmZlBOBxGqVRCLBaT0SB2QBKJBGq1msT5sgvABYw0+vv9fjHhl8tlnDt3Du12G2fPnpXN5Y7jIB6Pi3k+FosB2BpL6/V6E/tQisUiTp8+jSeeeAKrq6swTROlUgmtVgsAsLm5iYMHDyISiaDVamFmZkaSsvL5PNbW1hCLxXDHHXfAMAwcOnQIX/nKV3DmzBl0Op0Jrwc3xpfLZUxPTyMcDkvSFrsMlmWhWq1iMBhgZmYGvV4PnU4H0WgUo9EI/X4fR44cwd133418Pi9ekLW1NWxsbMhx2KGg+Ltw4QIWFxcRi8XQbDYnduP0+30RNXyfuaneu5yRizO5+JHiiN2OUqmEWq2Gcrkshv/5+XmYpgnbttHpdCQF7Gpz/POlynmXQ5JyubxtHURFURRFUZSdZN8Ile3yibwc0un0tu1QAbZ8EdzDAUDu6rPApsfDuz/FW0gy1pdmcBrauf2doobXKhqNotlsotlswrZtuTtP8UUjfbfbRaVSQTQaxcGDB5HL5dDtdvGNb3xDujwXLlxApVJBPB5HIBCAZVkibMLhMFqtlogdmu4pSMLhMO655x5ks1msra1JN+bcuXPo9XpYWVnBnXfeiWQyKUV3v98XIRcMBlGv1xGLxbCwsIDjx48DAM6fPy/+Dl6PRqOBRqOBSqWCbDYrnQO/34+NjQ1Z7JhIJLC+vo5+v4+pqSnZK0MR6O2ynDt3Ds8++yxKpZIkgDGBi6NW8XgcvV5PrnswGBQByE4OF1fy2nkjggnHudiZajQa0pXpdDqo1+siOvne5HI5JJNJFItFEUd8DRwl9AYyAFdGzPhaeB6O46BQKGzbZ15RFEVRFGWn2DdCZS/g+NF24TjOxLgQcOXudigUmogOBiAjSqZpSsIW75h7R4gWFxcnNpWzaPd6XOgZGY/Hsm2e+1e42+PYsWN43/veJwX62bNnUa1WUalUsLKyglarJSNKTBXzdlQMw5ARMAqYXq+HUCgkvhAW1xQ75XIZzzzzDCqVCqampmRXCjsv7AR4Ow2M8uXYGrsXPDf6OTg6FYlE0Gg0UCwWJzpVTBzL5XKo1+sYj8cyitbv97G5uYlCoYCTJ0+iWCwiGo0im83CsiwxrnNHim3b0r3gDphutyvnNjU1hVqtJks2+bmif4TLMKvVqkQYsxvD8x2PxyiVSpiamkI+n5c0Nvp+6Dfyjg/yc8DoYq9YoWDt9XryWeF53HbbbXjiiSe27bOvKIqiKIqy3ewboeIdXdktWCxuF17RwZElig4a32moZ2Hp3fdx9Q4MmuHj8bh0RuhxACAjYDS+0x8TDAbR6XSwtrY2UTADW0b9mZkZfPd3fze+8Y1voFAoSHfk8ccfx1vf+taJ7g8TwIg3Bti7JZ1m/ltuuQWJRAL1el1M/LZto1qtolarYWlpCclkUh4zNTWFSqUCx3HEG8NliLFYDK1WS5LPuGSy2+2i3+8jGo0iEokgHA7DcRzZX8LuBABZcMgdKvl8HnNzc8jlcjJuNR6PMTMzIx0s+mGY2AZcEVK9Xk8M6hRcwWAQpVIJlmVhNBrJ7hSKNo5qMb1tNBohk8kgHA6jVquJ8OCSR8uyMD09LcelMLIsSxLFBoOBRB173wuvL4XJX+z+8Jz5PqhQURRFUfYV4xFif/pNNN/9mr0+E2WfsG+Eyl6MfnnTubYD744MbwQtC1wKFgoVdk3G47EU1t5dJX6/H4uLi/D5fOJZGI/HMoLFx1L08K45i/dUKiXnxW4NOzrRaBRHjhzBysoKSqUSut0uHnnkERw7dgzz8/PinQAgBS89NBxpYzSuYRjiq1hYWMCBAwdw8eJF1Go1JBIJVKtVXLx4Ea1WC81mU8aa6Fup1+sIBAJwHAfNZhONRkO6IewY2bYtRXi9XkelUpkQJNy7Ui6XMR6Pkc1msbKygkgkIt6Qubk5mKaJRqOB73znO9LhCAaDMkoVDodlizyPbVkWut0uYrEYxuMx+v2+iA/6ePhesgvkNec7jjMx6kcfUbvdls+MZVnSiaOopfjg87Fj4h3pAiBi5OpFjnyv+fng58uyLExNTW3b515RFEVRtgt38MrrQeeADdu4De6jeiPuZmHfCJXt9IpcKxx12i44lhMOh5HNZlEqleRONpO3GFtLDwF9IBzJYfFpmqYkUNHbwSQpHisQCCAej8v3HceRx7Ibwi4Pi/pTp05hcXERoVAIR48exbPPPotarYZWq4UzZ85gdXUV09PTME0TzWYT2WwW9XpdFknSXO44DlqtlsTdck9JsVjE0aNHsby8jFarhY2NDfh8PmxubuIv//IvZblhNBpFOp3G/Py8FOjcy9JoNOS6RKNRiQoOBoOyM6Xb7WI8Hss2eYoHy7KkK8JxqfX1ddRqNaRSKfT7fbRaLdnrQmHJZYwUExR1fK/8fj86nQ6GwyFSqRSazSZc1xVhwyWQFCFMUut2u2KcH41G8jvNZhNra2uo1WoiYOfn5yXOORwOo1AoSFgCY6JpqucuFW/HjgZ6wm4Lf0bx3O12pSukKIqiKPuN+GceR+Nv3f6yf8/1GXADmmx5M7FvhMrVY0+7AcdwtotarSYGa96NBzCxI4V4TfXsrkQiERkRAiBLAzlixP0owNbYl+u6E4KG41Ec+2GcL4vcXq+HM2fOYGZmBsePH8ftt9+Ob33rWyiVSjh79iwMw8CTTz4pKV28q++6LiqViiRklUolKbwpCjKZDA4fPoxer4fz588jn8/LzhPXdTEzM4N2u43V1VW0223ZO0J/B4Wq4zhSVCcSCYTDYWxubor/hCNyoVAIlUoFuVxOxCHjgVnAMyWNxnbuP2GiWr/fR6/Xg+u6aDabqFQqiEQiqNVqiMfjUsyzI0V/DbsavKbsiHDvDbswHBujF6XVaqHdbqNcLounJhwOI5lMShzxpUuXUK1WkcvlZIRvMBggkUggnU5jY2NDxu/Y2RoMBvJ54vvl7ejx80YRw+unKIqiKPuRsWfk/OXSno3A/84XXhppjIHQQ994xc+v7C77RqjsdjxxLBZDuVzeVqHC3SQc3fKKDgoV/pN33QlFhterwhEtLg1k3DCLTv6TCVKWZSEajcpoGbBV+DuOg3w+L+NVTzzxBEzTxNzcHBYXF9FsNrG+vo5Wq4WzZ8/i7NmzOHbsmBS7TNIaj8fo9XrSVWHiFgvmfD4vRXSn00G73UYymRQvDtO8qtUqms0mzp8/j0uXLuHee+/FzMwMLMuCbdsoFovyehKJBFqtForFIgzDwGAwkOSzzc1NpNNpRKNR2R7PkatgMChLLbPZLGKxGC5cuCDXsNPpIBKJAIA8P0XShQsXkEgkkMlkZLs8uxLcas/f4/gaAOlaDQYDtNttjMdjSWWjSGGXhWIiFAohkUjAtm1ZPMnRN3ZPGLqQTCZltIyChPB7/Dvff563N/2r3+9PxD4riqIoys3COGBgbL/4lI7xjtci+D8f3aUzUq6HfSNUdnsJ3YEDB7C5ubmt3ph6vS7FID0FXq8B72KPRqMJIzrw3Nfv9/tl8aB3iWM4HJa4YGCrWDZNU7bKe0el6APh89N4v7GxgUcffRTZbBZHjhzB+fPnZVTNcRx87Wtfw8GDB+Xx9L/Qc0FztmmaiEajiMfjMio2Go2kuOdr5ev0iodIJIJKpYJisYhUKoVEIgHTNOHz+VAsFtHr9RCLxRAOh2U3iXf5o3dHzaFDh6SjEA6HYdu2dLW86WQMJHAcR4p8dlXoY+l2uxIXnMlkcODAASwuLgLY8ne0Wi0YhoFKpQLLsuRc/H4/yuUyqtWqhB24ritLK3k8iolQKIRkMolsNiv+klgshlQqhWq1il6vB9u2EQwGkUqlkEwm5dpyPwv9Mld7Y/g1OzlMD6PHxefzSdSyoiiKouxHkl9eQe2Nyzvy3L1kAMZb7wEABP7ysR05xo2C77ZjOPOjKfk60DKw8Ktf3cMzmmTfCBXu1dgt5ubmUCwWt3XkzBsnC2z5TPr9vvhOAEx0S7y+AXoOOLrFYpRih7HFhN4DCiEW+fSm0MvCbgPvrLOwLRQKeOyxxzA/Pw/LsnD48GGsra2h2+3izJkz+Pa3v4077rgDjuMgkUjAsizpAvEPR9MopryJVxyT8qaQhcNhLC4uyvnQk0Nj+mAwkI5Rq9VCv9+HaZrIZrPIZrMTwrLX62F1dRW9Xg9ra2uwLAtLS0uyCJLXfjQawTAMWJaFXC4Hn8+HSqUi3Y5OpwPHccQTQ0HEtC2O2x05ckQ6KO12e0L4+Xw+6Xw4joPNzU0ZjeNzAldSwphWlkqlJNGtUCjICN3FixdFYHivHT0qjuPI54iiBMCEEGHiGB/nFcmGYcjrVRRFUZT9yHB9A8lHgqjdNwt46qjtopvdGqWPvvFu+P7X3wB7sHh8L3Ffdycuvt3CID5G5FBdvj8c+nHpw6/D/L/eH2Jl3wiV3faoZLNZfO1rX9uRtDH6SHiXn+Nc3pEs+hy8W9CZzsRinYUuk61ovg8EAgiFQmKo9xb5LERpAPeey3A4RLVaRaPRgG3b+MY3vgHXdWVk6sSJEzh//jzq9Tq++tWvIpFI4MCBAxgOh1JQM4YXgJyrN3K52+1KEUwjP0et0um0mPIjkQiazaakXbGwpi+k3W6jUqkgnU6Lx4b7YyiYOGLX6XQwOzsrognARKHPLhe9KVzmWCwWcfHiRbTbbREcFI00tV+6dEmuJTs/NNJ7hQg7HMPhEL1eT4z3HDujiA0Gg3Itw+GwHIdLOfk5YRoYfTDD4RCmaSKRSEwIbAYBeEU+z4PjZfwe4XjcXkSCK4qiKMq1Mjx/EUm/H/XX5OH6tl+sAEB7JgzrgTtgPPwEMN4+O8B+ZvyGu3Du3SYiB2vPEQKBwAjd29t7cl7Px76pVLYzfetayGQyqFar2y6QmPzEcSMWsyysvcUhi2Lv8r52uy13z+krYGKYZVmy34PLIGn6HgwGME0T9Xpdng+Y9CsAkMQpwzCwvr4OALKzI5fLyQjUM888I8c7ceKEdAG8xS9wZQyLoosjYiycOarUbDYRjUYBQLwss7Oz6Pf7E+KJv+Pz+cS4703i4nVi54CjcJFIBIlEQlKwuL+F14X+E5/Ph2QyiWAwiEQigUKhMLHIkp09dnp4nXq9HpaWlnDrrbfCsix5XwaDgbyGWq2GZrM5ITjZDQuHw+LlAa4kxI1GI+nwcDGn67oiTr3nBkCWe/Lac9zLC3/O68bOG5+PnyvtqCiKoij7neHZFcSDATRuz+yYWGnNRxC759atrsq3n72uiOQbgeayicjBxgv+PBAcofjBB5D7yNd28axe4Fz2+gQIR5V2i0wmsyPiqFKpIBqNyu4Uvi6vQPEanDlGRfHhjShm8UrfSTwel03ovV5P4mgZXcwil50BPgfv9o/HY0SjUel2dDodbGxsiLclGo0ik8mg2+3iqaeewtNPPy3RwYcOHUI6nUYymZSRKhbyFEN8PdwrUq/XJcmMyVrD4RC5XA6BQAD5fF4ieM+cOYOlpSWJb+byx1KpJIs5+ZqGwyGi0ShyuZx0OThix8ex+zIejzEYDGTUjEV7tVqVa80Fliz6KQYNw5BoZ6/J/eDBgzBNU8zxw+EQm5ubst+FHRmKKQYeWJYF13VRrVZRKpXkc9Hv95FIJGR3DAB0Oh1ZvMn3i8/LLh2FLj9TfL/5vBQwXv+T17uiHRVFURTlRmD0zGkYt2bg7qCduXlga1okfjJ4cwuV++9A5VYDL7akIBAYof0WB/jIrp3VC5/LXp/AXsH43e3m1KlTeM1rXiOFIjsB3k4EPQv0FHjHdiKRiBSQrVYLtVpNRn96vR4sy5LRIG5dByAih56FqxOhCIUNU7ra7baY4HO5HEKhEA4cOIBKpYJWq4WTJ08CAMrlMk6cOIG5uTmEQqGJop/igAU1j8mIZMdxYNs2ms0mAMgyy1QqhWw2i3A4jNOnT8Pn88G2bRF4/N1wOIxoNCrnyTjmu+++G0tLSwiFQigUCnAcB/Pz8xIdXCwWJTWM58illdx4z43zsVgMoVAInU5Hdrp4x/G4s+WZZ55BuVxGKpVCo9GQ199qtSTRiyNvoVBI/niXQBYKBayurkpCWDabhW3bcF0XjUZDdrow+Ww8HssuFnZD6Ani54qCiNf++T5bXq9Su93ekc+/oiiKouwE9rkm3IAPrUUL44D+/+sVcd/tOPcuC+Fj9Zd8qN8/hvPD98P+xMO7cGIvzL4RKryTvFt4x2m2k1OnTuFNb3qTeCDYBeAIGP8OTEYWsyvB73FkqtfrwTRNWJYlRv3BYIBQKCTLD1lM09DN18UilnflvSlQNLxTuHAUiUskb731Vpw6dQqVSgWPP/44Njc3USgUcOLECczPz0vXguNYfP5QKIR4PI5+v49cLidLIenHGI1GkopFrw33nfj9fkkFYyoVC3Z2TegnSSaTOHbsGBYWFtDv92HbNp566imJR2a3iZ2R8XgsI3L0BXGrfDKZxNzcHGKxGIbDIS5cuIAzZ86gXC6j2WwiFAqJl8lxHNRqNfERxWKxia3zFIFcNkmRRdFTr9dx6dIlXLp0aSLymXtmCoWCCJ9ms4lUKjUhRLzmeQATP/OKEgo672eP0cT8bOx20p6iKIqivFLGf/MUAMAancDY3Pr/YC8bxjCi/y+7VjZeH0P4WO2aHuvzuai9twn7Ezt7Ti/FvhEqu1007VRHpdlsIhAIIJFISKwuo4qv9newaKb3g+NZLN5p1I7FYohEIiJeuACQu1VoGqfQeb677XwuXmd2P/iHez54rrlcDuPxGKZp4vz587hw4QIajQaefvppHDlyBPF4HKlUCrOzs5iZmZEoZe/4F68zzzmXy00klzFdi+NTFy9eRKvVEq/KeDyG4ziwLAv5fB6ZTEZ2kti2LUKNrzWZTKLVaiGZTIoHo9PpIBqNisGfYs2bhjU7O4tbbrlFRrMikQgajQZqtdpEl4hFPjsb3G8zGo0kEpmx0l6zPMftms0mqtUq1tfXUa9fuZtRrVaxubkpHh6fzyfHn52dlTE6+p9M05TjUgA9XygEX6N3xJGCai8WrCqKoijK9eJ+60mwkoredgztA3EVKzcx+0ao3Ex7HTY3NyeWN1KEcBwHgBS63nQv+hW80b0+nw/RaFREDbspLD69O1qYqOW9a07fg9e4D1wptjmaBEBia1utFsLhMBKJhOxnKRQKYhi/cOECTNPE1NQUjh07hjvvvBOHDh2S5ZTc/VGr1aR451Z4LqDs9/twHAeVSgWFQkHGkfr9PpLJpJjGvYlogUAAqVRKDPWnTp3C4cOHpesyHA4lAICG9Hg8LoKNG9zr9bpcP3pd8vm8GOiZNEZvDBdIxmKxia6FN52N76l3/IojYPSV0HPEfTCEXZxEIoGZmRl0u110Oh00m020221Eo1H0+30x5fM4VyfJsXt0tQD3imSel9dgryiKoig3IuMnnoblHkUvv3WDcxTxYWC98qCYcGUAbOMi8P2E/8hB9JLA7s4vXT/7RqjsNjt5R/nZZ5/FkSNHYFmW+CquTvzyGtz7/b6MEFFksNhmUU/zNbA1FkbR4TVrs1vAqF3uVWGkMX0SFDb9fl8ijSl0TNNEpVLB8vIyGo0G0uk0lpaWYJomzp07J9eu0+lgfX0dzWYTlUoFnU4Ht9xyi4iLbDYrhn/uYHFdF6FQSFK0Ll26hLNnz6JcLqPVasl5sIvCTgQLcX6vXq+j1WrhW9/6FhzHkeWOXo8OE9E4Muf3+9FoNFCv11Eul6VYByDnDFwxsVP0pFIpOT+KtmQyiVqtBgBybfn+2LYt7yvN8FyA2Wg0UK1Wn9P9GI/HsuQxGAyiVCqJgZ9dM4oKvlaOtvGz5Q1lGF31H1l+DiiY2VlSFEVRlBud0ZPPIPDk1t/NA0to35IDALgB42WJllBjhNCXHsf4JjXSn/+70wjeUdvr03jZ7BuhstseFRZ6O8Hjjz+O17zmNdJVoBeBULSwM8IRItu2EQgExHwObBnq6/U6+v0+Lly4IFvSuSvDtm2JNebeFu/iRwoEmtu93R0KFZrbDcPA9PQ0nn76afR6PXlsLBaTxK9Lly5JYhYFVKlUwvnz53HLLbcgn88jl8thampKzjEWi4kPplqtolKp4MyZM1hbW8PGxgZqtZoU9N7oXa8fo1KpoN/vY35+HuFwGP1+H6dOncKZM2fEXB+LxdDpdLC0tCQ+FnamuECTyx352ikm19fXEYlEUKvVxD/S7XZlQWOlUkG32xWBx/cPAOr1+sS+G362EonERDpXu92eGPnywt0u3m5aq9VCpVLB1NSUJK15o4f5nnl3uPDaUbTw88VunffaKoqiKMrNxPDceYTOnQcA+HM5tO4/8JJjYcHWCHCB0P/8JtxXyR6VG4l9U61cfRd4p9nJjgrN4olEQgQY79jzTjZfr3ffSa/Xk64HI36HwyHK5TIMw5DveZc7djoduK4r+1M4auT1YvDY3MlRr9dRrVaRSCTE3xIIBNBoNMQcv7Kygnw+D9d1MTMzg0KhgNnZWSSTSbTbbWxsbKBer2M4HKLZbOL06dO4dOmSHC8ej8M0TRFFsVgMg8FgwgsDQIpp7k5hMU1j/mAwQL1eR6fTQb/fRzQaRSgUQjabBQCJNy6VSrh48SIOHDggiWStVguGYchOG3YTOMLFvSbsthQKBaytrWFlZQWdTkc2wnvPiZ8dnj8N+MPhEI7jTLyv9Ml4Ox8vNG7FET9GOFNsFotFHD9+HMFgEI7jyHtFgeL9HLOr4hUhfM3eoIZwOCxCWFEURVFuRkbFIqyvjtB63eEXfVz4M9/YpTNSXgn7RqiwuN8tvHendwJ6C2zbhuM40s1otVoT40BM2qJAYRwt76iHQiHZzcLHOI4jiVrcB8NimQsLWZT6fD7Zbt7tdmUZJMejBoMBYrGYeGPK5bLE/UajUaTTaQBbY1eNxtZyoLm5ORw6dAjNZhODwQDlchnr6+vw+/0ol8u4cOECRqORpJbV63XMzs7KSFq5XMbCwgK63S76/b7sPOHoE/fQtNttjEYj8WXQZM5OxdTUFPL5PDqdDk6dOiV7YW655RZ5PnYiSqUSksmk+Eb6/T4ikQjS6bSMdNVqNbTbbUQiEcRiMSwvL8MwDGxuborx3zRN6XrR5G/bNs6ePSspahQaFFnJZFKuO9/3qwkGgyKsOHrm8/lQrVZRr9dhWZZ04Pr9viyx9Pv9Mn52tXeF36OPhp+JXq+HYrG4Y599RVEURdkPjMoVmH/+9b0+DeU62DdCpdVq7erx2InYKT73uc/hB37gB2T8iylNvIPvNT57x7HYYfDeFee+j8XFRUSjUQSDQViWhXa7jW63C9u20e12xafiXTZIY3YkEpGCPBAIoNlsTnQDOMo0GAzg9/uRSCSwsbGBwWCAubk5KYJphmf6lGmamJ2dxfT0tIgfjrsxzcvbaSmXy9LhoYjw+Xxot9uIx+Nibk8mk2g0GtIV6vV6sG1bOg+2bSOXy4lnIxqNwrIsrKysiKBoNBpYW1tDs9mEZVnw+XzS5WIsMRPV+Byzs7Oy6Z7Cr1QqoVqtolgsivADIGlpfA52OAaDgaS/MQabXYznEyqBQAC5XA7dbleemyNbjuOg2WwimUzK9wzDQLfbld9nuhw7Sd6kMq/Zn9HNfr//BQWToiiKoigKAPR6e2+93zdCZSdFw/NBs/pOsbGxgXK5LMUp7357x4C8Izzcl0JfCQtRFrvNZlP2kFjW1vZU27ZljIqpVLy7zjvrTBejABkOhwiFQrAsC8FgEI1GA5cuXUIsFpNuxng8RjweR7FYxMbGBnK5nKSB9Xo9uSPPPSGWZSGXy0nXY2pqSroL4/EYt912m5juW60WLly4gGAwiH6/j0ajIUKr3+/jwIEDWFpaQqPRQCwWg2EYIvQoOOiZWVpawmAwAADcdddd4uN5+umnkUwmRTRNTU1hdXVVOla8ptz5Ytv2xK6VTCYD0zRRKBQQCoUwNTWFUqmEc+fOyWvkTpTBYIBKpTIx0sXljFzwyKAA7qjxYhiGiD0mnTHOGdgaGSyVSiIEr4Zi92oTvddwT3HFzxS7NYqiKIqivDowxoDrGjCMa6t9BwM/Dr3vb3b2pK6BfSNU6vX6rokV+j12+niPPPII3vjGN0rRzD/eJCsWp/SjcEHjYDCQAp6P9Ubutttt8ZOwgC+Xy7KLA4A8H03qvNNO8zfN4JVKBYPBAKlUCtFoFI7jYGpqCrfffjvK5TLW1tbEp+H3+zE7OyvjRd6YZcb6sqPTarWQSCRkLOzIkSPo9Xo4duwYfD4fnnzySVy6dAm1Wk2CBAzDQKVSkbElLlHs9/swDAPZbBYzMzOYnp5GNBqV93FpaQmj0QjFYhFf/vKXMTc3h3w+j5mZGRw/fly2wPt8PllIyW5WIBCY8Gxwr004HEa5XIZpmrj11lsxNTUlz+PdWt9oNEQccrdLq9WSMTPHcUQkzM7OolKpYHV1FfF4HLFYDCdOnEAkEkGn00G9Xke32xXhQ+9Qv9+fGL+7ei8PhRcFEj/b/Cy1Wi15PHfeKIqiKIqyRxgGYFy+abgLJv65f/NVbPyj12H8pto1Pd5198cKg30jVFik7oZYyWQyu3JH+dKlS3BdF/l8XozR9GkAkNfL3RYAZMyHcJSH4z7j8RidTkfu/vPn3v0h3phediIokEajEQqFAhYWFlCv12VfSrPZRK/Xk87P+vo6EokE0uk0Op0OWq2WJHSl02ksLi5iampK9pqwO1Gv15FIJEQULSws4MKFC3j88cfhOI6MKVmWhQceeACFQgHlcllM/YPBAMViUbpK3PfBxZfpdBq5XA6pVArD4RDhcBiVSgWmaWJmZgZ33303HnvsMRlTO3z4MBYXF6VLBGz5h7gnhWEG9MIEg8EJ4z2DCUajkQijWq2GfD4vviqmi3FEj9Dkz+SwWCyGXC6H22+/HQcOHMB4PMbU1BSWlpawurqKjY0N8eUAV0a6KpWKjLN5PwdXJ+Wxe+LtzPC8KV7YzdvtUUtFURRFUa7wfU9U8fOpFQDAPb/8QWQ/+rW9PSEP3U4IB/dBNwXYR0Jlenp6146VSCTQbrd3RRT98R//Md773vcimUxKodjpdKT45gLHcDgMwzDQbrcnuiD0q1DI0fTOSFr6TTj61W630el0EIvFxDNSKBQwHA7FU5JKpeQOezKZxPz8PDqdDlZWVuC6LlZXV8W8T+8Ft6p3u11Eo1HMz89jZmZGRqmYNkXBkkwmZaTp4MGDuOuuu7CysoJHH30UlUoFjUYD0WgUmUwGU1NT6PV6MjJ17tw5NJtNmKaJVquFWq0mG+nT6TSOHDmCSqUiCWThcBjA1n6ZpaUlzM/PY2NjAxcvXpTt8tPT05KWFg6HpYPFHSsUJPTPUNAZhiHXghvhp6enxYPC73MZI4/HBDPGEjcaDfEFRaNRuK6LWCyG6elp+RzQV2PbNsLhMKLRKC5cuIBOp4NGowHDMFAul9HtdkXg0l9EmBYGQEQX09z4WiORCFKp1I5/9hVFURRFeS7vf/YsfshuyNeP/fJHgF8Gjv7OB7H8S/tHsOwH9o1Q2cm44KuJRCLP2W2yk6yvr2N5eVnugG9ubsoiP6Y2tVotxONxESMc5aEvxDRN8VuwOOX4VyAQkJQuy7KkgKU4yWaz6Ha7SCQSCIVCYsJfXV1Fv9+XyF6mbS0sLMDv98vIkuM4GI/HOHDgAFzXxfT0NBYXF5HJZKRLQ28MzeChUGhinCqZTOKuu+7CnXfeiS984Qv42te+BsdxkE6nJVgA2OoKHD16FJVKBY7joFgsyib3fr+Per0u4oSiLRQKyYLHSCSCY8eOoVgsSmfh/PnzWFtbQzKZxMGDB6VLQ7HCBC+GCUQiEQSDQYlv7vf74kPh+BWT1ijcxuOxjOtRQHojosfjsexmoceIpnYa+4fDoYx5EY4JVqtVLC8vy7JKr2j1xiVTdHkjiZkixp9xzGy3Pv+KoiiKolwD+2DaqtU0ceQnvrXXpyHsG6Gym9i2LR2F3eDLX/4yxuMxlpaWMDU1BQAol8sThnoW6ywymd4VjUZl7CcYDKJQKGB+fl46Ehx/6na7MuYDQOKQ/X4/otGoJGWxoB2NRlhbW8PFixcRDocRj8cxPz+P5eVlGQk7e/asXCMeI51OY3Z2FrZtS5IWR4w4etbtduU4TLKKxWIAtsbd3v72twMAPvOZzyCRSEiIQKvVQqPRgOu62NjYkJQt0zRhWRZ6vR4qlQpqtZoECIxGI7TbbaRSKYxGI4xGIxw7dgyf//znxdPCRYy5XG5i8SHH0ziilc/n5XXSnB4KhVCr1USwcVcJU8E4ypdOp7GxsSG7XryjVvQKAZDRLvpgAMheGL6no9HoOd4kpokRemI4Jsauiuu66PV6ACb3qlBQep9PURRFUZT9w3d+8j/gVut/x+Gff3hHnn/63z+CYvc+DN7x/MunnXIUR/+ff7PrAVcvxksKFcMwfgfA9wMouK572+Xv/TKAfwCAyxj+qeu6f3H5Zx8G8L8BGAH4Wdd1P3ctJ+K9i7zTJBKJHU/98uK6Lr761a8inU5jbm4OwNaYUq/XkzEwrzGaix9peh6Px+h2u5LSRSEBANFodEIocHs6uzccP+LImWVZGI1GqNVq0lGwbRsHDx7E8vIy7rnnHlSrVdi2jW9/+9vodDro9XoSUcy0rUQiAQCyGJHCisX0aDRCo9FApVJBJpPB/Pz8hFh561vfKkb9er2OZrOJeDyOlZUVGeG69dZbZbSsVqvhzJkz2NjYwPr6uoy/ua4Ly7JEjJimiXg8jiNHjuCb3/wmCoWCRCxbliU7Wbyjc/TDeNO1SqUSgsGgBCDUajV5fd7xMC6NpJji5nnvOJw37Yvx0PzscTEmu2b9fl+WefIc/X4/6vU6xuOx+Gqq1aoIIJr66X1iJDJHBymIKVpzuZx0qRRFUW4UdqseURRy6t/fjwMn1l7w5+dOzuDI//7Ith0vbATh+newNh2PMPXb30Clcy8675oUK866jWP/+DsY77MbmdfSUfk9AL8F4L9e9f3fdF33173fMAzjVgA/AuAEgFkAXzAM4xbXdV8yzmA3Fz5ya/lujr6Mx2M89NBDeP3rXy/7UHq9HqrVKjY3N2W5I8+P29JZ7DK5aTweY21tTe7+s8iOx+PSXXEcR8bHWLAy5YodiFKphOPHj+NXf/VX4bouPvGJTyCdTmN5eRmHDh1Cu93G0aNHcfr0aZimiUgkIulYsVhMdrCw88CCnoW53++fEEa9Xg+DwUAWSPr9fvzgD/4gPvWpT+HMmTMixo4cOYKjR4/KqNxgMJAxq6NHj0oCmeM4SKVS0klgUe/z+ZDNZnH77bfj6aefxoULFzAcDnHo0CGMRiMZlzIMQ7pBwWAQruui0WjA7/fDtm0xx1PMAVs+qkqlgl6vJ9eD72Gn05no0HC8i+KCHaNmswngSnjE5uYmjhw5IvtzGB3M41J08vrF43H4/X7xNHlhp4QCit0cjrTxeaenp3Hx4kUd/VIU5Ubj97AL9YiiAMCzH7kPJ46fh+kfvOBjzgZfvqh491NF/IBVBeB/3p8//Hd+A2vfH8B7vvYBiQd2X3cn/u1/++jE41puEP/nwde87OO7wyHSf/RN+P4sOvmD0Qgjz462/cJLChXXdf/aMIzla3y+dwH4Y9d1ewDOGYZxGsB9AF7SGcQOwW4Qj8dRq9V27Xik3+/jq1/9KhqNBm699VbEYjEpbsvl8sS4lPeOuneTfDAYFP8Fd594jeE+nw/hcFiWLbJg5qJAiob5+Xn8y3/5L6Uz8r73vQ+f//znUa1W4fP5cPbsWWQyGaytrSEcDiObzSISieDgwYOYmppCNBpFo9GQESevX6Pf7yMYDMIwDCQSCRmbisViz4nUveOOO7C2tiZpYjTDsxCnp4bemGw2i83NTayursK2bYkaZmeJpvRsNot4PI5utyueFo5P0avR6XREbFHUFQoFVKtVEQjcVJ9MJmUMbGNjQ7oVPK7jOCLSQqHQhIBirHSj0UCv15u4BowjHgwGkjbm9fzEYjEsLy+jUCjI9fU+LxPheD78Oz8L7MgBmBgDA6BCRVGUG4rdqkcUBQCM6PBFRcor5S7zAoLG84sUAJjyW5jyAzFrSzQM3n4P/uVHfxt3XVUnt8evfBLJ7fUwujwmvt+5nozenzEM4zuGYfyOYRiMEJoDcNHzmEuXv/ccDMN4v2EYjxqG8Siwu3tUEonErndUSK/XwxNPPIFvfetbcBwHyWQSi4uLOHz4MBYWFpBIJCYWNnKkioU/76yz4F1dXZWELNu2pXjnXhJ2JFhE0wNz2223TcQg27aNBx98ELVaDadOnZJlhslkUjbCM16XJnqKLBbGHEHjjg8WyZFIRPw3vav+xThw4IAU+8lkEvl8Xgzs3FzP0SqON9m2DcdxMBgMRKSwW8JO1cLCgoxVJZNJpFIpRCIROUd2VNgNarfbEuvMRZQcr+L2+Xw+j6NHj2JpaUmM8hQ9PBYA6Y7wfeJ7BUCEHUey2u02arUa2u22jH1RnFC4tlotiYg2DEPECce+BoOBPC/Hv/ie8P0AtoRyt9vFxYsXZbxQURTlJmDb6pEBboziTdlZTv3f34VbFjZf8nGHj6zjzP91/46cw3+/6/+DHzpZwM9/5I/wevPVu6T5lb7yjwA4BOAuAOsA/q+X+wSu637Udd3Xuq77WmB37+6Gw+E9vZvc6/Vw6tQpPPTQQ3j00UclPnd5eRmLi4vI5/MIBoMT0bP0OXBEqlgsysb2RqMhxS7vzvPOuuu6iEQisG1bRMrdd9+NH/7hH37Oedm2jUwmg1KpJEsM2bEZj8eo1WpSnHuXJfIcKSRM05SUKY5jtVot6XhUq1U5ZjAY/P+39+dhkqVlmT9+n9hP7EuulZmVtXRVd1c3TTfdDS0gMiLD4tcvIqgIo8KojMqg6Iw/Zxyvy2FGZ0a/qIjjCDLM6IwrKAgICM0q0Gs1vdZelbXmFpGxn9iX8/sj837yjazq7qqu3Krq+VxXX12VGXHiPecEzXuf57nvB5OTk0in0xgaGoLX65VNN8UNTfPm/BBTCNAXQjHn9XrlWhWLRVSrVViWJcKm1WpJVC+TwSgKWJFiZYRikdPrM5kMxsfHpfWLn2tWBNvtNhqNhoQKUBTwPbxOFBqtVgv5fF6u7djYGMbGxpBMJkWcmvNezMAF059C4bHW80Sx4vF4UK/XMTs7qwMfFUW5XljX/Ygfm9fdoWxf/CMNRP3PL1oTgQbckY0Rt3v9Ubw7MYf/N7J51ojtyAtK/XJdV2SmZVkfBfAPK3+dBTBlvHRy5WfPy/Dw8KYNfDQnw28VfNL+9NNPo1wu44477sAtt9yCWCwms0hOnz6NbrcrscP1el1awHw+HwqFAkZGRhAKhZDJZAaM7QCkGkO/Qjwex8TEBF760pdiaGjokuti6la5XJbNPqsG7XZbopXj8bgMOKThngKCsbudTkfWzgoJ128yMTGBxcVFmS+yNg0NwMBwQ85C4QR3tshxQCLPn1WXdDotYsLr9UploVaryaYfgIiuer0+MNeF96vX60mFZnh4GJ1OR6pZHBgJQAQUU7bYXsaKDgMU+LuFhQWp6vD9FIX9fh+ZTEZEGNPO2O5HEbl2cCjfy8qLZVli6K9UKhgaGtry/w0oiqJcLRuxH1EUZfvwgoSKZVnjruvOr/z1zQCeWfnzZwD8pWVZv4dl89o+AI9czjHppdgMNksQXQ71eh2nTp1CsVjEuXPn8IpXvALpdBrtdhuxWAyLi4uyyWUVgQlXjUYDnU4Hb33rWzE1NYVcLidJWpzLkkgkMDk5KWlWkUgEmUzmWddz7NgxMZXTz0HRwdkrhUIBiUQCtm1LehRFkWVZMuGdHpBGo4FEIiGbf27gyfT0NA4fPiyeC7OFiq81N91spWJFwfwdsJz8debMGXS7XcTjcdx5552IRCIiSMzkL6/XC9d1Zb4IRQWrLBRL7XYbtm2j3W5LwheDDMy2Or/fj2q1KteOVRsKOBr1OdvErLq0Wi14vV7k83mpWoXDYRFVTE1j69ra7zCvBTHbwPx+P/bu3YtcLodKpSKvVxRFuZbZiP2IomwUS5/dj1sDDwKwr/pYQcuH9v3TCLz27NUvbBtzOfHEfwXg1QCGLMu6AOA3ALzasqw7AbgAzgD4VwDguu4hy7I+DuAwgC6A91xOwgafim8WNJ5vF7HS6XSwuLgIy7Jw9913D/yOyVdMp+LEerYnxeNx3HLLLRgaGsLIyAgajYb4S9i+lEgkZJP7XHz6059GqVSS1Cia+ilCms0misUistksJiYmZNI9BYIZ1csKA4cxMmGLazKJxWLiETGHFAKr1S9uqi3LklaucDgslQVWV5iYVq1W4fF4EA6Hceutt0pwgWlApzhmuxjN9jwGz71SqcC2bfk5RQirP2y18/v9iEQiaDabMl+FVQ4zMtps+2J1yfSZ8DqwQlQsFiXpzLw25v9ueJ1YAeJ5mgZ7x3GkasNroCiKcq2wGfsRRdlI/vaO/4WEJ/r8L7wMvJYHXz7wKbwRV578dS1xOalfP3aJH3/sOV7/WwB+60oWwVajzWK7TeVOJpO45ZZbMDk5Ccuy4DgOHMdBo9EQQWVu5Fm5CAQCyGQy0uZj2zZs25YIYGC5Baher6NQKEgVgn4V0u/38bnPfQ7f+ta3MDo6Kq1e3LRzA8wWsmKxKJHIbO1iYhUAEQ1s36II4HHXRlFbloVGo4FcLidtXZxz4/f7Ja2q3+9LtHIoFJJ0LbZ+AZBY5HK5jFarJSZ3bsopLigszOoEqymsgnANFE+stHi9XkkBYwuaGVTACos5pyUQCIiAY6WD1Sb6YOiJYYscr3uz2ZSWNSaCUUQCq5UT+lZ4HvwdhRErbcDqrBVFUZRrhc3YjyjKlXB4cQw7Prl1nk+v5cHx/3039r/rsS1bw0azbSbTV6vVTatwcPO9HRgaGsKLX/xi3HLLLYhGoygWiygUCshms6jX6wMmbzMFq9PpSAuTaaQ2MYcuAsstT2bSF+n3+/jSl74kVZNAICDVCv6b8z84ed6stLC60+120e12US6XEYvF5DqzomAOnzSDAriGEydOiMChGDPTrUqlEs6dO4dcLiceC/pZKCrYGnfu3Dk0Gg3s2LFDrh8rUQAGjOdmWpmZqMV/mMRGEcMhlDt27ECtVhNPC4Ukz4GikOfPNXA99JxwtguFE2OlW62WXDMKIK7f9KVQ2FAYUriY7WGMZ6b5XkWKoiiKorwwnjg3hdiDNlKLPYQ/tX4DH18Ip1/3Mez5g5/Fvvc9DGyTTqH1ZNsIlc3cOJlzLrYKj8eDiYkJ3Hnnndi9ezf6/T4WFhZQr9exsLAgc15YBeHmlxtZigoAAxWDtZhiIxQKydyUtTAemE/uGTvM9iOazAHIGsxkKZ4TY4rZKkVhxeoAvTdrhUogEMDc3Jykew0NDYkXpNfroVQqYW5uDufOnZPKDKs4vC4AZADjqVOnZEAlKxahUGjA92JOmDevM8ULz4OvY/sdK1c33XSTCEG2jZliOxwOIxAISOWF143XimugQDHTyOjx4ets20YoFEIsFhMPEK8pqz78h2sw/T29Xg+xWEy8MWv9LIqiKIqiLBP9agTn/t8UdsaLF/3u8bNT2PHJAMKffGALVnZpZn74w3j9v7kH7jabKr8ebBuhwgF/m1FVMWdRbAWhUAgTExO4++67MT09jWq1inw+j6WlJfESAKspVGyd4tN1YHnzyaf55jXj5HlOouekdhrUL4XH48HLXvYynDt3DrZti0mfn1epVBAOh1EulxEMBmXSurnR54aaPhRzCCQrCQwDCIVCFwnFcDgMx3GkmhSNRhGLxTA+Po5YLAbHcbC0tDTgn8lms1IlYttVvV7HhQsXUCqVMDQ0JHNR2BpXKBQGhBQAMbubLVkUQBRErOqwjQsAxsfHMTk5ibNnz4pg4L0xE9cYS7xW3K1NNGN7HH/X6/UwNDSEarUqRvxoNCr3kj4Z83isWPF4AGTgZCgUGniPChVFURRFuZihjzyIrO/leGb04oTUHU/0Ef7kchXFNzmB7Gt3AgD8DRexv35oU9d5I7BthMpmznXYytaXYDCIXbt24e6770Y6ncbi4iLy+TwWFxdlU88WJFYE2u22bJ5pUm+1WrBtG7lcDkeOHMH4+Lg8sV9YWIDjOGLgjkQiSCaTSCaTGBkZuWhNHo8Hb3nLW/Dxj38czWYTfr8fxWJRKg3NZlOGPkYiEQwNDSEWi0kCF5OrWPXhcEb6PFgB4usvJVQAyDyRXC4HYNm7U61WcdNNNyESiSCdTsPr9cJxHHS7XSwtLSESiYjosCwLhUIBFy5cEOHC+SW8ZgDE02JGIZvihOdNwcWNPUUSBVcsFpN2MlNg8DOZYBYOhwdatfha+klMLw9hFLVt21IFSSQSCIfDsG1b1kiRw3VSJJnhFLzWrNKZs28URVEURbmYkT969oqJb/c0ynePoXSTFz/+jvsBAMdrozjWfRkAwOpDxIxydWyb3cpzPfFfb7jZ3wqxMj09jbvuugvRaBQXLlxAPp9HpVKRNi62KHFKOTfIpVJJDNAUM5zl8YlPfAL79u2TJ/alUkkGPdKg3e12sX//frz0pS9FKpVCNDqYOhGPx/GqV70KDz/8sAgBtoEBkHjedDqNdDqNQCCAer2OSqWCarWKRqMx0JLU7/dlg06zPOeSrJ1O77oucrkcbNtGLBZDtVpFOBxGIpGQysrQ0JDMdWGVKBwOo1aryQDJcDiMarWKcrmMWq0Gj8eD/fv3Ix6Pw+PxiOmd62L6G9u9zOnu/DvbwMz47Eqlgng8jmq1igsXLkirHACpNJmGenMQpunRYUWD3iOzEkXhTvHKdjPXdUXoMbSA15DfDVM0mm1rFENc63ZJvVMURVGUawXfrp2Y+YkJvPOt9w/8fH9kEft/Y3msT7Pvx9drLwcABL54cNPXeD2xLYQKKwibBedwbDbJZBIvfvGLEY/Hsbi4OOBFMQ3QfLKfTCaltYsTzKPRKLxeL9LpNOLxOGq1mng1uAmmr8Qc1litVnH8+HGcP38eL33pS3HffffJZ5L9+/fj3LlzWFxclCqI4zjwer2o1WpIJBKYnp7GyMgIAoEAqtWq+Cq4Ee71erLp5lyQVqsllYpoNHpR6pfjOFhYWEAikRBPSjgcRiqVQiaTQTgcFvEWDoeRTqdFOFQqFeTzeUSjUWQymYEZLJFIBNFoVIz85XJZ/CJ8DdPTTCM8N/OsSlBABINBuTfhcBgnTpxAPp8HAPHBNJtNmRvDa08xwbhkXq9OpyPXgJHHPA7jkbmedDqNYDAoAQX834vZ3mUKb1aT+HuKc8Ye8/wVRVEURbk8fFOTOPlTE/iXP3j/c74u5Ong9R/4BgDgq1/cvDmB1yPbQqgwknezaLfbmyqMgOXWtle96lWIx+M4c+YM8vk8ms3mRSlM3KzST7A2kpgT5vfu3StVlWQyKZ4IioJGoyEtY9x0c8P9yCOPYNeuXZKIRSjgOGSQ7UZMtUokEjhw4ACSyaSIona7PbDhpshglDCPx2oQz8XEHDK5a9eugRkmTKvikEvOhWHVwXEcGbBIETE+Po7z58/Dtm0MDQ0NRDTHYjFJ4GJlh3HN9LK0Wi1ZIwUPX88ghnq9jnw+j3K5POBHYSWL7WHxeFzilgGIMDLvSTAYRLlcFlHBFj9eQ8uypBoDQL4L5sBLU9TQE8Rrws81444VRVEURbl8fBM7cOI9U/ip7//yVi/lhsLz/C+5/uCT9c1s/WILUqFQEA8J24y4sTR9DsBytYBP9RkDnEgkMDU1hdHRUQSDQfErMC3KnC/CgX/csHIierFYxBe+8IWL1vjMM88gn8/LIMJarSaiLhQKIZVKIZlMShwwhRDXzI0/f85KEP0TwHIFYGxsTD6z2+3imWeegc/nw9jY2ECUL9fAsIFKpSLVBG7QaW73er3I5XKwLAvpdBqZTAb1en0gtSsWi4kfhe1arNSw4gBA1sCJ9Jx14vf7pTWL58dqD7DqEwkGgwiFQlLB6Ha7CIfDiMfjMr2eVR5WYPh5ZjAB74PjOGLIN4UO58uwdQ2AXA/+bG2sMT9X56goiqIoyuVz8uemX5BI8e3Ztf6LWcNnauEN/4ytYlsIFW5oN4t6vY5wOHxR69NG4fV6cfvttw/MIzHNzDSZc54GN761Wm3AJ8JNqhmvzNcy1tecRh4IBNBoNKR6Uy6X4TgOOp0OTpw4IVUKYLn96mtf+5q0ZWWzWRQKBUkA46aYG3RWHyKRiBjrKVqAZW8FB3lSkAUCgQFvjOu6OHXqFEqlEkZHR0Wg9Ho9NBoN1Go1qRyw+mReQ7MylkgkxMvjui6mpqYQDofl2tD/Qq8LPTjmJh+AtIoxbtisjgCrrVasorDywihlVkp4/Vkx4yBLpm6Z16BWq4lQYlXG6/XK36PRqIgc/t22bRFMphGfFS5GGfMzGJ3M75IZlawoiqIoysbwqk8fAjwb28Xzke97zXUZTQxsE6HS7/dRqVQ2zTdSqVQ21Ux/2223IRQKodVqoV6vS1vP2s2yWSmgEOFmMhQKYXh4GHv27MHo6Kg89W+1WjLBvt1uo1KpSNsX/SrxeByxWEzM7IVCAaVSCR/5yEfEEP+Hf/iHOHfuHKrVKqrVqrQr0STv8/lQLpextLQ04Dnxer2Ix+OwbXsgXjoUCqHb7SKTyaDf7yORSCCZTMrmGViubP3TP/0TLMtCMpmUlrNSqYRKpTJgBmerGasDsVhMNt1MB7NtG8By9Wnnzp0YGlqOFWTVqd1uy6R7BgpQZDWbTXQ6HRlmSdO6WY1i5DOT0WKxmKShhUIhGdDJe0kjPAB5bywWQyqVQiwWk3WxpY/3kZUb/oyipVaroVwuSxWGhv21/5hzYDhjxxyOySqeoiiKoijPjycUgrstzBKX5nMPfhbWdZrmuW3OipvOzaDf72+qUDlw4AAsy8L58+extLQ0IEb4BJ2VEf6M/gMKjaGhIaTTaYyNjaHVaqFWqyEUCqFSqUiSFp/w0+PRaDQkfYvGbq/Xi8XFRdRqNXzuc5/Dxz72MTQaDbz61a8GAKTTafj9fuRyOXQ6HZTLZRFZ5XIZU1NTmJiYQCKRQCaTgeM4qFQqAwKEm2K2G9m2jUgkglQqNfCar33tayKq+Fq2eCUSCcRiMcTjcamAMKWLPgvOOaFgotmenhNeZ9PzU61WEQqFxNDO6gQAEZEUj6bfY23Lmeu6SCQSUlWq1WrS4kWRaQYc0JtTKpWwsLAAACIcTBFBUWSmu/GYTPRiG5iJOZHebMMzxR6rYKz0aOqXoiiKojw/x377Tvyr7716b0qhH0C6v9rNErWC8Fr64PC52DZCZTOf8BYKBSQSiU1t/aIhnBteGujZUmTOHqEvheJlZGRE1huNRmUWCeeHsOrSaDTQarXEN+H3+yUVjD6OUCiEqakpnDt3TlqcLMtCPp9Hr9fD2bNn4fF4UK/XJXWMZu5SqYSZmRlkMhlMT09LNYGeh2QyKZUciibXdZHJZAYM9M1mE1/60pdQKBTkaX8+nxfvxsjICFKplJjY6cFh21mn00EgEBgQRwBEMMTjcQDLG/Zz585heHgYwHJ7Gzf+gUAA4XBYNu9M2+J9YYoW2/JYsWm32yLQ5ubmpJJCgz+vicfjgeM4aLVaco8CgQBqtRqazaZUnNhSZ66DVSO/349ms4lEIiHfI4/Hg0ajIWvk8c2wBQAy2R5YTQRj+EAgEBBxpiiKoijKc2BZgLU+D/Z+bfdLB/6+99EQ/seEDol8LraNUNnMOSrZbHbTzMTT09Pwer0oFAqSpAUMCjO263AjzgQqDlqcnp7G7OwsxsfH5TqxwsCWIjNy1nyiDixXClhJiEaj6Pf7CIVC2LlzJ/bu3Yt+v49sNotSqYQLFy5g586dmJiYkAGKHD4YDodx5swZmecxOTkJ13UlBrhUKkklotVqYXh4GK7rIpVKYXFxEV6vF2fOnMHc3BzOnTsnno1kMinnwBYwcw4J25+AZW9FLBZDIpFAsVgcMJPH43EZFskKg9frRSKRwOLiIjwej/heuEZef1ZkOp3OgLeFVZtYLCZCoVKpiOCp1+vSWsd1+v1+EUPZbBbtdhvRaFQqZ4ynZvqWGTFstvxxfRQWNPKbCWLm/27Wvp9VJcuyEAwG0ev1kMlkZAinoiiKoijPzfGP3o2ffdlXN+TYp+5t4of+6bX45E3PHXd8I7MthAp7902Pw0bCVqXNqKjcdNNNMoeE8b9s++ITcRqu+XNzrszU1BS8Xi/q9brMIDEN2cFgEM1mE7FYDN1uV4zm9FfQA+H3+9HpdDA/Py+igwZ8v9+PdDqNRqOBxcVFGS641tfBRKr5+XlMTk5idHQUnU4H9XpdTP4UQaaIyufz8Hq9OHToEB577DHUajUMDQ2JH6RarYpYGBsbE28MqwVsXfN4PIhEImg2m1hYWEAwGBRhxwrJyMgIRkdHMTMzg1QqBa/XiwsXLiCVSmF+fh4A5NpSWPC9HGIZCATguq6kfbXbbREJzWYTkUhk4L3xeFxSvprNpgzajEajqFQqEu/MljJ6TBggwX9Ho1E0Gg35XrKykkwmpWWLlSmKDVOcsMWM3yce25wP4ziO+JsURVEURdlYvvqiCIDeVi/jmmXbCJXNbkUJh8ObUlFJJpPo9Xqy6WYFgBtz+hEADLQIAUAmk8HY2Bja7bZshAGIYOAMD3OKOQDxYPCpOtO0yuUy2u02YrEYOp0OCoUCUqmUmMDHxsawf/9+FAoFRKNRFItFVCoVpNNplEoljIyMSLvU6dOnpVIQiURkY08zPDfj7XYbjuOg2WyiWCyKCKJYmJ+fF1/I2NiYVFCq1Sr8fr+0xFEMsNWMbVwUUGwDS6fT6PV6OHbsmIQH0PhPbwevPcUcY5u56afHh5/Pti+fz4disSj3IBgMYnx8XIYxMg6ZQjAUCqHZbKJarUpFB4AkcDEq2BzKmE6nRbA7jiPVI3q4AoGAJHoxrpmilWKK3zNgdVglv2NcH1vEFEVRFEXZOmrfs4TX4S4AwPEP34PTP/DRKz7G6ybvBvrXZ6fEthAqHo/nIr/BRrNZAx/N5CgapcPh8ICZn21cFB2mATwUCsFxHNkY8+m+1+sVv4r5c1Zk6Ovgk3S/34/x8XHE43E0Gg1kMhkUCgUUi0UxiWcyGXg8HgwNDWFsbAy2bWNxcVGe5tO4z9albDYrVY5YLCbr7ff7WFhYkMpDKBSSuOFwOAy/348LFy5gaWkJp0+fht/vx1133YUdO3ag0Whgbm4OPp9PhjMCqxv0arWKVqsl18fc7Luui9HRUczNzUkrVzqdlsoNW8TMaF7OSWElhC1SnIlCwcQEL5/Ph1KpJJ87OTkp81KKxaKI0Gq1KveIAzJZ2WIljR4Srq1araLRaMDv92NoaEgqKmz9onii2OJ3xRQr/L1ZXeH3C4AM0NzsgaeKoiiKolwCo5No/899Bzc5P4uTP/bhKztG//p9+LgthApbYjaTeDy+Ka1f4XAY1Wp1YAo9RYU5pI/pUfQcBINBSaQ6d+4c2u22mNZNY7xlWbK5pcm+Wq3CsqyBqhFFEDfK/Nxdu3Yhl8vJ5pdVGM4loT+Gwuf8+fMIh8MyX4QCi+1FY2NjUvlJp9Myy4XrXFhYwKlTp+A4Dmq1mnhdRkdHYVkWzp49i8OHD2Nqakr8KpFIRAQLRRdnszQaDSSTSfkdAMzOziKTyUgFhhv1eDwuZnwOswyFQvD5fBINzOtAL4tZZalUKlJ9YQuW2SJmXutIJCItXZOTk5iamoLrugNT4avVKrLZLHK5nFxHy7LgOI6EJbCSxHkrrLjR78SKHAc7skLE9bH6xtk7m514pyiKoig3Ij3Xg29+1xCA2uW/qd+D1bv8/3/uuX38wK2vBlC50uVdM2wLoWIO1NssNkscmW1GzWZz4Mm8KVZMg7fP58OePXsk1YumaD7p5/rpL2FsLzeiAAYSrEiz2ZSqAr0yrVYLkUhENvkej0eM+9VqVV5nDlPsdDpijucU9ng8jmQyiVQqJTNYuCk/e/YsOp0Ocrkc5ufnJTggGAxidHRU0roWFxdx+PBhNJtNme/CY7NCUq/X5bzYMlapVDA1NQXHcXD+/Hk4joOhoSGJBKbpnZ+TzWYRjUal9Y73ybIsVCoVqYrw+jOJjW1T9Jew6rO4uCgtZqw+hUIh1Go1lEolAMsVIVNUWJaFdDqNdDqNHTt24NixY6jX6+JFMufn+Hw+OI4jfy+Xy/J7ilx6eACImKLXqd/vSxqbGbygKIqiKMr6U+8H8OhrxtCv5a/4vTe9/ync3P45HPuXf3xZr+9Vrl+RAmwToQLgunzCOz09LYMETQHBTfGlfAKsUtAgz9eEw2E4jjMwvZzT3yl4KCq4YTWf0hNzPgiPTy9GqVQSPwY9Jdy4s62rVCohHA5j586dGB4eHkgRC4VCWFhYQLvdRrVaRSQSQafTwdzcHNLptKSOBYNB7Ny5E7VaDel0Gh6PB2fPnkUul5P5LazqpNNpdLtdlMvlgc03ryljecvlMqrVKpaWltDr9eA4jiRnsT1ubm5O5sF0Oh2pVrCFjFUjfj4DB3itGWTA1qlOp4NSqSTX3LZt1Ot1qcYsLS1haWlJBlQCuCgsIhQKIZFIYGJiAidPnhRRy3YyVlPYUmf6kIDl1jV6atgCxu+a+R0wRSx/riiKoijK+tNzPegtXblIAYB+rYa9HziMA82fx+Gf/x/rvLJrj20jVDZz4CMAaZvaSG6//XYAQLFYRL1eHxAUZiStWQ3hk/pKpYJkMgmPxyNzSChAWA0wN+yc1E5TOM+NyV/c/LLSwdYwVmMcx4HjODL1nmuKRqOIRqOoVqtot9sYGxvDjh07JKWt3W5jbm4OuVwOk5OTGBkZweTkpFQkotEogOVWOxrsc7kc9u3bJx6Ns2fP4jvf+Y60VKVSKezfvx8333zzgCCiyOB3hdey0+mgWq3izJkzACACplwuY3FxEUtLS5iYmBARWKlUpF0rkUhIKx1N+p1OR6oYZpWEIoQVGopI0xdk2zb8fj8WFxcxOzsryWBmGheFKkVLLBbDzp074fV6cfbsWfEg9fv9gRkqDEUoFosDUdf8DlGw8P63221pZQMgbYdcq6IoiqJcT/jGRtGbHH5B73UfOzTgF3mhLHWiOPSOmwCceMHH6JXK2PXHR3F75+fxzC9eWqy03A7e9P+8E8ChF/w51wLbRqiYAwE3A1YiNpJ4PI52u418Pi+Gb3o5uLllhcA8f7ZacWI7k7UqlcpApDCjeel/4Yaa7V188s8qgNk2xo1yKBTC0tKSDHi85ZZbxNTNgY/AcpTv7t27kUwmRbgUCgX4/X5MTU1heHgYk5OTSCQSYvTudDqIRCIy04ST2YeGhhCLxZBOp8V3Q89KMBhEJBLB/v37ceeddyKTyUiCFrCchFYsFpHJZKRN7ujRo1hcXES1WhVh1Gw2MTs7i1qtJm1rACRxrFwuS7tbp9ORyfUUdqYZnf4axi3T3+I4jnyP2LZFkXDhwgU4jiMVIYoUChDeI4qYYDCIvXv3otFooFwuo16vIxaLSfXM9M+wtYvrN7/HHLTJljaPxzMgXJg2ZlZXFEVRFOVaxjc1id5QAl2/B/3ACwuL8d55YPUvh0/CNebRXS7nm2mc/Ll9cI8884LWYNLLF7Dzf53A9z3yL5G/NYTHf31ZsJzuOPiZd/4C4ALex79z1Z+z3dkWQoWbts1kM1pf2Cq19um1OePCfMLOn5sVF1ZMuPnn5pznwJalWq0mn2n6b8wNqWnGp8maCVjhcBjf/d3fjZtvvhnFYhEnTpyQtKxer4eRkRHxyszNzaHT6WBoaAh79+7F+Pg4/H4/gsEg0um0bKxpCqfXpFQqSUuWbdsinkZGRrB3715cuHABXq8Xw8PDSKVSSCaTEu/suq4kcvEc2u02yuUyTp8+jXPnzmF8fFxilWdnZ5HP56W1i6103OCzYsJjsNUrHo/LIErCWSjtdhsnT56Ue8LEMNu2UalUkM/n4TgOEomEDL/k8Eomd/HemRHVjK/2er0YHx9Ht9tFoVDA1NQU/H6/zM3hveD7KXAZFsDfUZiYFRQzjMD0vCiKoijKtYxvegrd0eQLFiikF1l9YOzzevFCHmW3+j64B69epMiacjl4v5bD+OMJvGru3QAAT8dF8GuPrttnbHe2hVABLu7d32j49HujuOeeexAKhQZahLhhpEDg57O60W63RZQAkA12rVZDMpmUdrVmsykmax6XG1mKEW5WuaE2W8f4Hsuy0Gq1EA6H8fKXvxwveclLEA6HkUwmceHCBTSbTYTDYZnpwbakqakp7NmzB2NjY0ilUtJCxuoCJ7yzMpTL5WRKe71el+GKHPTpui6i0agMe/T7/Wg0GpLExWtlVgKazSZarRaOHj2Kxx9/HD6fDzt27EC73UatVpO2LbZEscLD47GVa21lIRgMyua/WCyiXC7j+PHjWFhYQCqVwszMjBjhLcuSc2O6WKPRkKSwYDAoccCsnnQ6HXi9XsTjcfH+mOKTxvhOpyPigqKG17bVag3ELJuJbaYApwhmOhgFcL/fRy6X24ivvaIoiqJsCr7d03DDIXQjgasWKRdx0054jp9B30jqfD6O10Zx6j/diiDWX0T0SmXYf//Iuh/3WmDbCJXNxnGcDW1/OXDggJjAzcngjPIlfOLN1iaKFgqNTqcjT/i56aQA4FNzeixMUURjNQWEmQJmbvrr9TqmpqZwzz33iOGbSVpcc7FYxKlTp9Dr9bBnzx7cc889GB0dFSM719poNNDpdGDb9oAfp1arIZ/PyyyVeDwubVH0pcRiMWQyGczPz6NSqaBYLKJQKCCRSMjnmJPYq9WqeFvOnz+PXbt2IRAIiB/InDtDIUePilm9MgUjU9lY7cnn8zh8+DC+9KUvicDauXMngFVfSjgcRrFYRCwWQyQSkeoWW+4oIE1fEO8D09b4vTBjjlnh4Rwetn55PB50Oh1J8eK9Ntv7TGFsJszx77FYDOfPn9+w776iKIqibCS+XTvRHU2g79uYMRPdeAg+vx9oNrHnL1186uuvQe51Lbz7zm9d8vVPViZx7vf3I/L5hzdkPTcyN6xQqVarGypUOFCR808ADCQvcTO5dvAeBYjf70ckEpHkKT6Rb7VaksrF6gpbskzPgvn0nK1SfLIOYKCa8j3f8z2Ix+Oyhk6ng3q9jkAggPn5eZw4sWwIu+eee3DgwAHs2rVrYD4Jk804v2VyclLWxLkgrDREo1GpprBywQ00DeJsEaMPJhKJwO/3o1KpoFKpoFqt4vDhw3jsscekwsF/fD4fWq2WfDYHINL/wnPmxp8tcBSQrLhQXJ06dQrnzp0DsLzxP3DgwMC9i0QiUp2j4GQMMYUgBSe/A1wjhSE9JYuLi5iZmUG/30cymZT7SVFiVsFolOe6TQO+GYHMtbAV0OfzIR6PY3Z2dj2/7oqiKIqyKfimp9AdS26YSLno8776GBIAYufvwkd+6rvxr+765sDvHy7uwtxH9iLxtw9tynpuNLaNUNlsj0qxWEQikUC1Wt2wBCQasFldYFuOaXCmT4FPvQk3pdzwr42dBSCbUiZfmZPXTcO1bduyeTVTn/r9PkZHR7Fv376B4x49ehT9fl8mxweDQRw4cAD33nsvhoeHxVPDuSaO44ifpd/vo9FoyLDJarWKTqcjT/tjsZhUULimTqcjHg8zhWxubg6O46DVakk7VzabhcfjwenTp3H+/Hm4rjsQI0whxFkn5rwVVi7MwYxrW/BoeG82m3AcB/Pz83JdLMuSqGFeR7ZeAZBQg0gkIr+nYGG0sNn6xu8BsOyDOXv2LA4ePIjR0VFMT0+LEGVMMQCZas8/89z4HaHoWntOwKo4jkQiqFarV/v1VhRFUZRNxTc1ic6ONFzvxu4Z/Qtl9JuDZnrPNx7HHusl+PN7Xzvw89CSi/RfPLih67mR2TZCZbNZXFzE+Pg4FhcX112osOWIk9eB1U0xsOo5ACCbS5qquYFtt9toNpti7uYG1azAMHaYT9Nprudx+RTfTDhj2xDblvbv3z+w9larhYMHDyKbzeLs2bOIxWJ45StfiampKYyMjCAYDMqkecdxUKlURKTw6T5nffj9fiwsLAykXTHVi+lblmWhXq9jaWkJ1WoVPp8PkUgEhUIBpVJJUskWFhakyhKLxQa8IJyVQsN9PB4XccC2KV5XM23NnDlDs3soFJIktXa7jWw2O3B9OOSRG32zna/VaqHX6yGRSMDj8WBoaAi2bUskNAVHJBJBJBIZCFWgr6fVag2kujGIgAEIPBa/I8QU+qx0rR2MaQ6yVBRFUZRrje54asNFCgC4c4twOxePzfB+/TvY8fUN/3jFYFsIFW7UNpPZ2VnxWTSvwCx1OYyNjUnbD5/Y88m7aWg3qySmWAqFQmKsZ9wsN6k0adNQbR6D097pW+E1pTeDAwo5/Xx4eBh33HHHwNpPnDgh1Yp4PI5XvOIVuPPOO2WT3+v1xItSr9dFCACQ9jS+rtVqoVgsyjmwpYmRxX6/H61WC47j4OTJk6hUKgiHwzKHpFKpwOv1wnEc8fp0u10RCdy8F4tFSRUbGhrC8PDwwNwSCgjeC7accV1MLKNQofGc18uE6x4bG5OWLJ/PJ21+nU4HyWQSIyMjGB4eRjAYlPtL0zxjh3k+wLKoGxkZwe23345QKCRVIgpAijImjZmYXiRWVkwhzOOY6WOKoiiKci3hHR1B17s57V7K9mFbCJVutyvzOjaLCxcu4LbbbtuQ+S27du2S4YbNZlOeZvMpOTfRTPgyxQZbwfhUnL9j5YCtRAAG2oG4uaafhdG13MQ6joN6vY5yuSwb2UgkIgMFgeWAgUceeQQLCwsAgJe97GW4++67ZbI7/RFmuhV9M6wCBAIBRCIRtFot5HI5tNttxONxxONxtFotJBIJWR/9KPl8HkeOHAEAqYKwSkJo0Ge7lRnBDCy3XS0sLGDHjh1IJBJyzvF4XCpL5kDMRqMh1ZBgMCi+Ga6BbXQUUySRSCCVSkklq9FoIBqNwuPxoFarIRAIIBgMYseOHSIwOW0+FouJl8j0wlBUjI6OIplMSmWJ5x2JRER0cSCl6Usx/ULmIElzXo45y4VVN0VRFEW5FvAOZdDdOw7X8/zVFG+rB09tpUXa50E3Hrqiz/IXG+h3Nn6EhXJ5bAuhAgzO+9gMstkshoeHN0SojI2NDcxCMVu9TN8CMZ948/WM6OVrY7GYbHpZbeHx+NQdgHhVuFnt9/uoVqsoFotSCaGgGB4enN566NAhHDt2DP1+H/feey8OHDiAeDyOCxcuIBwOA4CkdLGVi36Ver0uT/rHxsbk54FAYCDBKhwOI5/PIx6Po1QqoVQq4cyZM7Lhp1ChQAsEAgiFQtJaRfHm9/vF7zEyMoJQKCRJYNPT0yJAmHLF9rdarYZ+v494PD5gUGf7FlumXNdFPB7H1NQUTp48CWBZpOzbt29gfkq325VWtnA4jPHxcUQiEblfFBWs2phDJk1xAQDhcBiZTAaHDx+WqGmKSXpulpaW5JisjJhJZrxu/F6YwzbpsaFwUxRFUZRrge7+ycsSKQDgKdfRO35q+c+xGPDivVf0Wf2TZy7Z9nV5H+6FddctQB9wH7++J8ZvFttCqHCexGZSr9cRj8cHWrHWi1QqJQbzta1eZtKU+bSbm0x6U7rd7iXb4cwqhunD4Obd3KQCy1WEXC6HXC4ngikQCCAcDmPPnj1y3Fwuh/vvvx+nT5/GXXfdhf379yORSIhBHVjeXDcaDdn0Ulj1ej1Uq1Xkcjm4rounn34a4+PjCAaDSKVSsrH2eDyoVCpoNBoIBoNwHAcLCwuYmZlBuVwWYcGWMp/PJ+KG7Vps9aIA8fv9Iri63S6y2ayIG3PzDiy3isViMWkVY0Qz75XZStdut5FIJHDXXXehUCig2+3i7rvvRjgchuM4Ythvt9u4cOECnn76aYRCIdx+++2Ix+NScWF1hp9F0cD7yyoV70mpVILruohEIrhw4QLGx8fl+hUKBVSrVXkvhZzpQWJFzkyBMw39gUAAhUJhvb/yiqIoirIheONxvOD6Rq8Hn9OGa1kDAx0vhc9Z3nO5/SubsecJhWDtWR5d0Lf9OPbuMOBauPVDgx7g3uHjV3RcZZltIVQYmbrZpFIp2WyvJ4zEBS5OXer1elLRoGeC5m5WKsyWL1ZV2PrEqe7ckLKKwXYoChdWTxi5S39EOBzG0NCQtGgBy1HNn/nMZ3DixAkkk0ncdtttGBoaAgARS5zO3mw2xf9iChUAMoWeFZJ9+/Zhz549mJ+fl0QwJl/Zto1qtYpTp07JcMlyuQzbtgcSuzhbZHh4GCMjI1IVsCxLKmIUKq1WC5lMBrVaDR6PR6oPruuKEObgTFYhGATAWSe2bYtY8ng82L9/P4LBIBYXF/Fd3/VdYthnu129XsfnP/95FAoFuK6L7/7u7x4QwBSVjuPIezmtHoC8jiL1zJkzACDtc36/H/F4HIuLiwBWK2Zs9zJTzAi/S7w35kybUCgkccuKoiiKsp3xRCLovmgP3Mv0z3u6fVjt1a6Bfr0OPHEYVjAI3Hvrc763/9QxoH9l4UqeUAjde27ByR9fI4IsF0d+MTHwo1s/sAdWd/n4vfOzcLvaXnY5bAuhAmx+PDEA2fCuN6avhCKBU8rpW2i329Iaxo2r6UOwbVuerofDYdlwBwIB5HI5qZ6wPYi/N8VDrVaT6e9kZGREKiTZbBbJZBIPPPAAnnnmGeTzebz2ta/F2NgYQqGQbNj5D9fMFima1Fn94GfPz8+j2+0inU5jx44dsvkvl8sSFNBoNCQxrNvtIhQKif8CgMwAqdVq0hIFLLeV7dy5E5VKBfF4HJFIBOPj43LOrutidHQUwLLnw3VdLC0tyXUtFovi76DRnYMZzeSwUCgkovKmm27CHXfcAdu2xVNihgjwc1lB4vFMv1Gr1cLS0hKazSamp6cxPDws7Wasipw7dw7FYhE7d+6UuS/095w+fRrNZhPVahWu68K2bbRarWetCDIhjmtlbHIoFMJXvvKV9fuyK4qiKMoGYAWD6N617/lfaODLVtA9s/EP4zyhEDzJBNo378DJt1/eVvrIv83In2/9UAi9IyevWBjdiGwLoWJG6W4mGyVUvF6vGLCZIsUn3F1DQXO2B43b9J+w4gIApVJJTPHcHPPpf6fTkUoU55qEQiF4vV6Ew+GBAZA07bOK0m63cfLkSZRKJTzwwAOYmZlBJpPBvn37kEwmpeLB6g79IXyC7/P5kM/nZeK8OSmdrVRstUqn0zK8kseLRCLodruoVCri1eh0OgiFQqjVanJ+rAJw008z++TkJAKBAGq1Gnq9HpLJJCzLwvz8PEKhkPhaOA2+0WhgZGQErVYLpVIJ8/PzmJubk7bDaDQKn88n7Xpsket2u0gmkyIwGY7QbrclYvmHf/iH8fGPfxx33XWX/I7hB16vF4FAANVqFTMzMzh27BhuvfVWvOQlL8HCwoJcy1qthnK5LPcwEAhgenoaruuiVCqJuGXIQKfTEY8KBRVb45guZ6aG0TMUj8c3PbhCURRFUa6U3vNUQNZi9V3gWfzO1vOEyHi6l++TtoJBNL73RTj7pita3gBHfiGOW/5wH6zZxed9rdvtor/Rs888XngTRmeT20evVN7Yz7xMtoVQ2aokIj5BX29Y6WC7Dze0/DzT38G4YdMQz+pKrVZDNpuVyexsDePU+l6vB9u2pT2L0cFsTaKXpNlsyuea09TL5TJOnTqFEydOoNfrYe/evTKIkbNW2GIUDofRarVg2zaazSZ8Pp8khjUaDZTLZZRKJZTLZVQqFYyNjSESieDcuXPI5/PYuXMnAoGAtG+5rotisQjHcdDtdlEsFuHz+VAul8UHk0qlEA6HkU6nMTo6img0img0imQyOTDRnt+dVqsls3HC4bAM2+TEeGDVpzI1NYXZ2VnZzHO+C6taNN3z3pkihq9jK97OnTvx0z/900gkEgiHw1IpoXG90+kgFovhzjvvxK5du9But7G4uIh6vY5EIoFoNIpYLIZEIoFSqSQiZ3JyUmbyBIPBgXhrVkrowWF1hRUcilTeKyaPmQlmiqIoirIt8Xif/zVr8M8W0D17/uJDhcPoPp+h/uGngcvYD1o+H2pvvBMXXn/1AVBH3xsDEHve19nn/Zj+wHfQX+dRGoLHC+vFt+DIz4blR1bLg32/8PDGfN4Vsi2Eirlx30zohVhvfD6fbKrZzkTzczgcltYp88k3sFzl4NN4elEWFxcxNjaGVCol76cIYcwtqzSsnHADz7azUqmESqWCZDIpPhFWH+bm5pDNZjE0NISJiQkMDw+LUKEfpFarIZVKoVgsIhaLyWT2YrEo7V9M5aI/hIKCU95ZQaEX4/Tp0zh+/DhmZ2cvStHiPJhoNIqxsTEMDQ0hHo/Dtu0BscYBl/Tl8F7Sw0GjOoVgKBSSakI0GsX09DQASPWGFbZOp4NAICCRy2yho2+ErXYUogAkeSwej6NWq4lfiBWOZDKJaDSKVCol95YzZdj6NT8/jzNnzsgxPB6PtMaNjIwgn89L5YmthTTW854Tfif4Gq6hUqms+/ddURRFUdaT/stfdEWvt9b/mfPFeLyovPUezP+zzW3Xakx1cPrX7sKu//wY4PbX19tiWcBLb8OxdwXX75jrzA0tVLLZ7EAr1nrBzSWfbFNMBIPBAZHCTSSf4lNkcE3lclnSpRzHkSGAwLIBPhqNim/E7/eLOGGLWLvdlgnvsVhM/CH0uhQKBeRyOUnQ2rdv30A7EaswNKCz0tPr9VAqlTA7O4tqtSqzRnw+H/bt24ddu3ZJC1QikcDQ0JBUBBYXF2W6fS6Xk4hhv98vIQFTU1MYHR1FLBbD6Ogo0um0tLQxEYznPTIyIq1iFDCBQEB8PtVqVSKSK5XKQAWLVQimjZmzbvgeJohxsvzQ0JCIA3P4ZjweF8EErJrjOZm+1+vJPWDFhd4kCo2hoSGEw2FUKhURgIx6ZjR1qVQSQWXGKVMImxHIrK4Ay1VL27YxNze37t93RVEURVk3XkBLvv9MFt3ZDfr/t5X1FH/8pci9fGsM8K3RLo799xfD63ix998alY4X0hVkXN/e99yFUz925dWrzWRbCBUA6z4d/nK4cOECEokE8vn8unlkbNtGLBbD6dOnZYMajUbFb0GY6MW2IwADczDojwCWfSoTExMIBALiaWFlhhvsTqcjvhBurBcXF7G0tIRIJIJUKiVP7imgzpw5I61GyWQS6XRapr7bto1er4dwOCzigEll/CxzYx6NRjE5OYl0Oi0DITOZDLxer1RDmMrV6XQwNTUlvg/OiUkkErBtG0NDQ1KhCIVCInLMVj2/34/x8XHkcjmpcLCiwBkpTPFiZYXeHXN+iRnlS6Fnxi/zNQw0sCwLS0tLGBsbE0ERCATk+rAljP4YhiFwIrxZaWGssNnCZt4PXme2blGc1uv1gbhj/sPPpnAxQw4ohA8ePLgu33NFURRFWXcsC/1XvHhdDuVNpdC5bRpX2zez8IvfhcqtHeCFhySvG71oD8c/fM/yX1wL+3/2kSs+RvntL8Piq64dE/+2ECqu626Jwffo0aO4+eabsbi4iFKptC7HTKVS0ipEzM0mANlU+nw+Mdlz02+a7jnjo1gsDszOaLfbktxlWZZsvFn14Gf7/X6paKRSKdk4W5aFY8eOoVgsSvTx3r17Yds2HMcRb0W73YbX65XhhI7jSCUim82i1WpJbHA0GpXY31qthmQyKefK8+Qmmhtttj1xM53JZJBKpTA0NATbtgdmqDBy14zlbbVa0s7FKg2w3NLHgYtmhYRQcNGYzjhpMz6aQyxZfaFPhd4gmvy5ft4DVs7YIma2dlG8sGrVaDQkUAEAisUivF4vyuWytLaxCsbWOl5TAHI9Cat3FDBcD4MBzPQ3RVEURbne8E1OoL1reWTBejT2z/7qy1Hbs02HJFsujn/4pZctVrLveTlKd3QAXDsiBdgmQmWzp9KTw4cP40d/9Edl0N56EA6HZSPKBCtWSizLEpM2xQTbqShQzI0nN/U0qafTaZk0z4232ULGSF0KCyZuxeNxBINBmYPi8XgwMzODarWKkZERZDIZ7Ny5E5FIRCKEm80mWq0WotEoFhYWRLA0Gg2ZKB8MBjE+Po7JyUkxd1PccANfKpUk3avZbGJ4eBj9fh/lclk28fF4HF6vF5lMBuPj4+Jt6ff7SCaTCAaDUh1h5YRejUAgINHDwLI4Y1WIRnQGGXATzwQ0s12O72W1CoB4eVgNoghhSxhbvlzXRT6fF+GSyWRQr9fh9/tl8CPvN31IFD4MQmDbVj6fl2NSEKVSKeRyOYk3NkUPRbDH4xkYrGkK5V6vJ+JMURRFUa432rtHgN0jeCHz5D3feuKin535ze9Ce6gHWNtUpJAVsXJ5r93m5/IsbAuhwiSqzSaXy2F0dHRdE8eWlpbEQM3ZGvQK0PzNf1NgMK2JMcXAcsQv/Qy9Xg/nzp1DIpFAKBRCKpUCgAH/AaslfKru8/kwNDQkMb2tVkuqVoVCYaA1aXR0VMzubDHi7I98Po9CoSBtYB6PB+FwWFK4wuGwVB3oi+Gf+TQ/l8shk8kMtDoVi0UUi0Xs378f7XYbO3fuxNTUFMLhMCKRiBjcG42GDL1kixVbmvgamv5ZzWFssW3bMpHd9IowBplxv2zt8vl8qFQqcBxHUtbYpkXhVCwWpdWK4QSstHBWDv0y9KmYQsUUqbxfwLIoogmf7XmJRALdbhfVahXNZlNECd/LtjKz5YvrNQMFvF4vgsEgzp+/OA1FURRFUZRVZv7bd6Gb2vo2r8tmU5IEto5tIVQAyIZtsxkZGRGPyHrAli0+9abJnPNRuEllApY5uZ6vZYXJnDrPeSXckPOJPCsxNJKbvgt+PpO+uLktFArodDqIRqPw+/0y54SbXW7o+/0+8vk8fD6fGMEtyxIPSa/Xk0oJAJnyTpM6P5cm+VgsJgMLa7UaEokEvF4vJicnMT4+jmg0KtUSVmgajQbC4bAILraARSIRFItFBAIBMc9T6NGcTiHgOI60bFGosOWKYrHZbCIUCsG27YG5MaaPholrFH1+vx+xWGxgpgmvEc9j7T2mIOU9ZugAgwJarZYM6eTn12o1FAoFAJC462q1KtcagFRVGE3NQAca/uPxOD73uc+t2/dcURRFUdYVjxfufbdv9SqAS89RvmHw1LzY9+8fx3aRP88rVCzLmgLwfwCMAnAB/Inrun9gWVYawN8A2AXgDIAfcV23aC3vxP8AwBsB1AG803Xd7zzPZ8iGe7PhZnm9MFuL2GbFOFoA0n7D1h5uMGmgN83R9CZwQ3rhwgUxuff7faTTaQCQjTf/TZFibpQ5MJKxuuVyGSMjI6jX6wNP/02fCtOlotEoIpHIwDDCer2OarUqHg/XddFoNESosUXK6/VieHi5X9SyLKkOWZYlaV6pVEo2/OFwWL4P9XpdzPMUKYxgZksWW8BYNWg0GkgkEqhUKtL65fF40Gg0pMLFdbDljpt7BgPwfrH1iveCIoM+lmq1ina7LZHNDBgwrzkFKys6tVoNkUhEKkKmeOV1pIhhWxgHVjabTaku8X38HrEyZ1ZX6A8Kh8NIJBJb1mKpKIqyHmzGfkTZWlzP+g/hvlxO/X/fhX7AhRu8sdukLRdwt9HMtcvRjV0A/8Z13QMA7gPwHsuyDgD4dwC+4rruPgBfWfk7ALwBwL6Vf94N4I+f7wOYWrUVrPfmzZyxwY0jjfJsUeIGkpUcCgq+nhtUPi2n6MlmszIHw7ZtFAqFi57aMz2KbWMUJ5xans/npcphxhTztTx2IBAQATE8PCyCgr8zE8qazSYqlQoajYYkVtEEH4/HJbGL7Uvnz5+X6OI9e/aIEOJ7WOXgkEJeAzPqmVUcni9fz6oTAImDjkQiMoOF14qtd6x0mLNugOVqFq8Jr3GtVpPP7Ha7iEajMieG17/dbsv1obDh61m9MlPFKCx4r1ltKRQKIopqtZrMbDErTXy9z+cTARkIBCQlDViu2KRSqS2J/1YURVlnNnw/oty49EN9uKHedd9Kda3xvELFdd15PoFwXbcK4AiACQBvAvBnKy/7MwA/uPLnNwH4P+4yDwFIWpY1/nyfs1WtX+VyeV1nqVQqFRkeyM2haZBnSxETpwAMtHAxxater8u0evolFhcXkcvlpJrA6F1WLvh5rMKYs1AogqrVKsrlskxu73Q6KBQKkkDV6XSQSqVEWNGTwmPTc9FoNGQDTVM7zzkYDIowYGUmGAzK4Em2gqXTadi2jUgkIoKDaVf08dCvQb+FKZCYvMXoZ4oHtkfxvrKyxepPPB4XIWFedwo9muw5qNKsdNEXQ8HA+GQKGwoNVlMYdsD7wQQu877Se2Mmv1Fs8VqzisOKE//3wmvEa8B7TyhiZmZm1u07riiKshVs1n5EuXHwHTwKYNk87/pVoHgrPtz8gTNbvYwBrugxq2VZuwDcBeBhAKOu686v/GoBy6VYYPk/GqZr98LKz+bxHGxVItHs7OzAHIqrhZG5fPoPQDbTrJTwSTvh5tuET9vNCeTA8kwVJkdR6DCIgKKEG2VWAygg2CbFTTpjgpvNJur1Our1OiKRCCzLks08k7tMfww9M/V6XTbe9ICw5Yx/54aeCWasCCWTSTGrM5GMwoNtaEzfomgyvR5M3WKr28TEBEKhEPL5PABIq1YsFsPS0pIIoeHhYXg8HoRCISwtLUla2NqULBrS6/X6wHyVZrMpAQkUVjTPm98BVs5onjd/T8HD+S3tdluEjjn3pN1uw3EcOI4j991sD+v1egOVIn4HWH2iQASg81MURbmu2Mj9iHLj0G82ce4/vhztoa5WUgBYPaA7v7DVyxjgsi1DlmVFAfwdgPe5rlsxf+cu75Ku6A5blvVuy7IOWpZ1kE/Mt4ITJ06IkXu94Obb9J+wRYcigRtKbsppYOfgQb6ePguKqGKxiEqlIhv+tU/STYHC1i5uVlmtobjgMXu9HgqFgjydZyWn0Wig3++LIKFHgm1HHMjI84pGo9IexiGHPP9CoYBKpSIRyawmMCnMnNjODTurRBzYSKM529no64nFYlJhYsIaj8O0MralRSIRCQZIpVIiOugrIvxOUrSEw2G5vhQTbCFj21Wj0YDf70er1RJhSoFpztExvSW8X2yh42czBKFYLMJxHHmvKeg51BJY9TyZ1TOuj0JUURTlemAj9yMdbJ/efGVj8T9zGgDQHFORsp25LHVgWZYfy/9R+AvXdT+58uNFllBX/p1d+fksgCnj7ZMrPxvAdd0/cV33Htd17zE3zZvNE088gR07dqzrMLy1T9i5YWUqEwCpNrBywif+yWRSJrlzc2u2PVUqFSwsLMjcFHpPuJnnxpgVCLYTcUPMKgzFSDAYRLVaxezsrDzl53BJM/6Y4oAb5XA4jHg8jng8Lu1RAESAcJPM6k+pVJJ5Jpyb4vP5YNs2KpXKwOR2bv4pBihiufmmd4PXOJlMotFowHEcWTPbsvr9PkKhkLRY8VqZUcoUhcBq5DPFAq8x2+3YFlav18VPxOvMChEFGMUP7/3av7PtzBQrpgG/0Wggl8uh3W4P3BszeILT63mfzIoMKz5nz55dt++2oijKVrLR+xE/1i8FVLlC+j34j258jL7lAv4j59ArlTH/b16uImWb87xCZSU142MAjriu+3vGrz4D4CdX/vyTAD5t/PwnrGXuA1A2SrLPynr6RK6Es2fPYmxsbF3nuLBqwY0rsNreRdM8qypsl1pbUfF4PNI+RaM4RUK5XB4YcMgqBTfOrusiFothcnISsVgMjUZjwFRvJoIxqevChQtYWFiQVCmKgGAwKBteCqhIJIJoNCr/JBIJxONxaXnzeDyoVquo1WrI5/MigigIIpGIXBcKG64RWG2NYyWJUcL8uVmdcBwH9Xod5XIZZ86ckWGPZlWBFRzXdVEul5HNZpHP55HL5VCpVAauO+8D284IPTy8dxTXrGQxophVHnOuDaFXhW1e5nwdU1Ty2pRKJdRqtYEZLAAkvYz+GNMDw+MBqxUvFSqKolwPbNZ+RNk6evnChh7fcgH/zALm33YLsu95Oar7r80hiBuBr+zD7k9vv+6Ly/GovALAjwN42rKsJ1Z+9msA/huAj1uW9VMAzgL4kZXffR7LUYAnsRwH+K7n+wAzknWzaTabSCaT6zr0kX4Lc3YGMNiew6f6hE/wOdzPnDRvRhSHQiFUq1UsLS0hkUjIcMRarSYb7UQigX379mH37t04evQoDh48KK1ENG7TL+M4DhKJBBYXF3Hu3DkRIaFQCI7jSPuZmShmGtXNIAB6YBzHQa1WkzQrVkwAiOBZXFxEPB6X6F6KCVYkWH3htTJDAViB4EafrVGcuUIzfb1eF/8LZ7tQ1AGrlS5eX96vdruNeDwu5xQKhaR1jDNxKDQoINfGTbNtzBQZ5n000804yNHn8w20aBWLRdTrdWQyGankMAGOYpLCjdee15j3yefzSVqZoijKNc6G70eU6xer78I/W8Di9+9G6Q4VKGvxNgHrwSe3ehkX8bxCxXXdbwF4tmDr11zi9S6A91zpQrbKowJAKgHrRa1WQyaTueic2OJFTIM4hZrpOaEhnJtgHqPb7SKXy4nhne1ENMgPDw9jYmICN910E6LRKCqVCh599FH0ej2pBPDYbBfLZrOYmZlBJBKRSg7XR+HAa8UWJr6Ga6J/hWlg5kaaG3WeN4/pOA7C4bD4YJjOZZ4XW5rMRCuz1YpVF27mOY9l7RySWCwm7V5M7qLYoaCgp4PmeQYBsI2MccSsrrCSQY8KfT6mwCIURvxMXkPeA1ZJAEjbFys5vO48p0tVIM0IYrYL6jR6RVGuFzZrP6JsIa6LwPmVUJypzFUfztPuwbdYghsOYe61wwjuDSN/7zU0dV7ZHpPp127oNhuKg/WiXq9jYmJCRAk3rvw3cHEcMz+fG1xg1ZPR6/XEsA0sb0Idx0E2m0U4HB6YGs82LX722NgYXvSiF+HUqVM4ffo0Wq2WtAjRQ1KpVNBqtXDq1CkRCJlMBul0WkzxbEniZpkbZYqeWq2Ger2OWq0mVRUOfaQPhjHG3JRzHkgsFpNWr0gkglarhUAgMNAaZZrJGbnLa8E5LfSGABAhRK+KOTyT4s/0iJhT7ym++Pd2uy3pZaywMImMQontXnwfrz/vpTmpngMz6d8xfTN8XTabRbFYhOu6qFar0oJHsUf/Do/Jz+LnMpzg5MmT6/StVhRFUZSNp3t2+QFbwONBeyJ1Re/1OW14ystdBN3RBPIvjsJeCqNrWyjfplWUa5FtIVQAbNnARwASaWv6QK4Gx3EGWtm4+eTm0vw5N6hmpYC/4/vMn3MjzESodDotbUoejwfxeFzM12Rqagp33XUXzp07h263i1qtNjBDpFarIRqNolQq4cknn5SqzK5du9BoNJBKpaSKQB8FABEt1WoVlUploP3IFCPRaFTWb7ZL1et1ESKshKwVc3zt2njitdUOJm6xhYzCyjS8U6TQkM4EL7Z/8f7w/RQVrHiwgkOxRTFAfxCrKbZto9FoiEeFSWVsBzTbtcxz4xrr9Trm5uakxSybzSKZTMK2bTkXCjczaICVrVAoJH4ZepkURVEU5Vqie/os/D4vOqPxy3q9t9aBNZsDQkHUbxtHbdSH/D1aPbnW2TZCZSuZmZlBJpNBPB5HsVi86uMVi0V5Ah4MBgf8D5fC9F/w6T3bq1glYXUFgHgZ6vU6stksMpmMJG+xzYktQsByKta+ffswNDQE27ZRLpelIsHj0Q+zuLgIj8eDubk5OI6DfD6PdDoNy7KQTCbFP0OR0+l0UKvVBuaCcOo7TfJmshZntQCQczU336bng/+YbVRMxqIhnbNEeH3pHeFgRjPpi0LBrFxQnJjVFgCyHg5gpFeIx+OcEjMRzRSWXDsAWTOwGhTAahbTxzh7xrIs5PN5mZvCljK2hfH6m8KVazbb4Pr9vrZ9KYqiKNc0vRMz8HtuAjzL+59uKgzXY8FXasJaecBb25tCI+NFqBxAMDqJ4s2hlfYuFSmXi6fuRfL49kw/2xZChe07W8XTTz+N7/u+78P8/Py6CJWFhQWJxWUiE0UGW67WTo/nhpmbcW5u+YSfBnIKmGaziUAggHw+j8XFRezatUtarRidaxKJRDAxMYFDhw4hHA5L7C7fw412IpFAq9VCu91GoVCQigkFELDc2mYOY2SVgJtxRiDz59xwmzNjOOiQfhhOpOf1AHBRapYpNFqtlky0p6eFXhOKB4pFXl+2a5kT6FlJMSOC2f5lWZaY6Ckio9GoCByfzycVC95PXn9TaPKczHPhfBpzLg3nnWSzWREv9OY4joNYLCafzzY0AAPJcD6fTypY2valKIqiXKtY99wOADB3M7VJG30vEHMBT2v5/wPzt/lQ28OuHB9UoFw5wSUPEn/+wFYv45JsC6ECYGA2xGZz8uRJvP3tb0cymVyX45XLZQAQgcEn8/RF8Gn92janXq8nAwgBDEQJr22NM6Nq2QJGnwp9Fc1mUyonkUgE09PTmJiYEMHDTT+HXXJjzwStsbEx9Pt9SQHjrBZuxPl327alQgBAPpfCga+hUGFFZq2HxxyiyOvFdQKrVQ62v/HaUgjVajWp5HQ6HcRiMYkabrVacv15nU3/i3k9aahnix0FFcWj6ZlhmhorGWZrF4UP7zGrMvSW8ByZotZqtTA7O4tcLodms4lwODzQvkaByKGS/G6YFThGEvM7qCiKoijbFSsYhGfv9CV+YeHIz9iXeMdyFWUBfgDcN6r35Gqwml6EF7dnNQXYJkLF7LnfKjKZzEA71NViDhBkFYUb90uZoM1WIWC1jYdP7bvdrkxHZ0wxU6uazSYqlQqGhobEbH7y5EkMDw/j1ltvBbBsrt6xYwd27twJy7JkBodlWchkMqjVatJeFAgEUK1WMTY2JhUFfjYFEzfZrIKY7V0csMiBkq1WayA1jAKEAsdMtuLvSqWS+CzM1jBu0s02KBr++VmsSlFUxWIx8cFQSACrs214TqzY8DVmHDLvTbvdlvPLZrMDrWN8LatUrPbwHlOotNttmV7Pz+73+1hcXESxWEQ+n5eKXKWyPHSZ95nXbO13jOuIRqPSuqcoiqIo2xnvjjEc/sXEVi/jhiZywYPM/9ye1RRgGwmVrTTTAxioZKwHFAPc9NJnwKf0ZiQxAKlGmFHE9EMAEPM1W5xYpeFskFKphEqlglQqBa/Xi0KhgKWlJfFYhEIhDA8Py+/j8TjC4bBszNlKxs01vSZseWKKFgdCUhBxA81qDDfNTNviZp8eGvomuFm3LAu1Wg2BQEBalugtCQaDci1ZiaBQMa+P3+9HqVQS8dVsNpFIJETwmClnbIszh3KaHhPT/+I4zoCopBgwgwV4b3hOrNawlW1tZDA/h5UdisxarYa5uTnUajV5n8fjQTQaRb1el/vImTT0C9Fw3263EQwGkUgkUK1WVagoiqIoinLNsy2ECp8mbyXcIK4XNHUTPmFn9YGmamDwyTh/7/F4YNs2SqWSCALG9pqT6ulhqFarqNfriEaj4sV4+umnEY1G8eIXv1h8QGzjYrsQPRUUM1yXGUXMNVLAsE0JgLSwrTX7A8ttUclkEt1uF+l0GsDqDBVWS7ix5/Ft25bPoWChgOEgQ9d14TiOnFO9XhdhQ6HTbDYRj8dl8COrVhQ6FFtmmhe9M6x8mPHFFCmcmdJqtUQUUdSxIuO6LsrlsggcVll47xh20Ol0UC6XkcvlUK1W0ev1UK1WB2a18HpWq1URqv1+X4S1KRpjsRg8Ho8OeFQURVEU5bpgWwiVdruNQ4cObekslQsXLqBWq63rTBdupDl5nO1cZgIYN7bEnBPCDTxFgBnzy2oHW66azSbK5TJs25Y2qjNnzgAA4vE4pqen5el7Mpm8qJWJrUjAagsU29ZYFTEHOK6NyQUunvvCmSp+vx/5fF5azXjOfI054DKZTIopnMLCtm2J7WWKGAAZsBgMBmHbNmzbRiQSwenTp6WqQf8HK1IUQgBE/LESQv8QrwNFGdvI1rZ4WZaFRCKBRqOBSqWCWCwGYNVbZPpU2N7Wbrdl4nyxWEQul0OhUJDzqdfr8Hg8Inz6/T6azSYikYi0gZl+Hv7j8/kwNDSExcVFPPbYY+vy/VUURVGUDcOy4NrBrV7FjU3fgqf9/C/bSraNUDly5MiWCpVvfOMbyGQyGB8fX5e2mUqlIk+96eVgypY5ENBsBTMrK4z9DQaDKJfLIlhYCaFgaTabImJKpRISiYSY471eLw4fPoxer4d//s//uVQh6vU6gsEgotGoJE3RdxEKheA4jnhAwuGwbN7ZXkRTudnGxKoD/8zZI61WSyok1WpVKjTBYBClUgnAsjCKx+MyB4UVC7/fD8dxUCqVpMLCakkkEpHEMK/Xi1QqBb/fjx07dogYaLfbA1UHc+ijme7F712n05FqFmOlTW9RMBiUQY2m4Ox2uyJS2FZXLBbl+GY8MsVOPp9HNpuVygvDD1hRYyWI17XX60m7F6s5bGEDIPHS9Xr9qr+7iqIoirLReG/dhyPqT9lSkod8GPnv29efAgDPPtxjE+n3+0gktvbL+p3vfAdjY2PYsWPHuhyvWq0iEonIU34AIgZMAzarBoFAQHwcZlSxGWtrmqhN70goFJKn/nwqT1EUi8VQLBbx8MMP4/Tp07Kx5edzlgknyPP4bB8LBAJico/H47IeiglGA7PK0G63US6XpRJAT4g54JFtcDTUczCkWamo1+uoVCpoNBoiILgx51p5/diuNTY2JhWjtUZ5y7Jg2zaq1ap8BtuvzOQ1Tpinn4ThBby2pjfFHFTJ+0sRQq8L10qhxcGY5vvpC2q1WohGoyJOWFnid4E+GwCSngYsC71EIoHz58/jmWeeWZfvr6IoiqIoylazLYQKALzmNa95zqGIm8G+ffswPDy8LsfK5XIAMODzoDfEnO5uDjgEIG0/0WhUNvQcLsjqSSQSQbVaHfBjFAqFASM6BQ/bpi5cuIDZ2dmBie00jZtzVzh/hBWCer0upm22lPn9fgwNDSGTychnsjLE6gawmuZG7wuDAprNprSF1Wo19Pt9aQHrdrtSSTAn1bPdi8fjefAY1WoVS0tLWFpaknhjAFLNMKs+XC8AOTdg2UcUiUTEIxKJRGQQI0UMW9kYktDr9RCLxVAulyUymJUYRibzM9hmViqV5LW8PxR2lmUhlUqhXq9LdQWA3E/eO35XACAWi4kYVhRFURRFuV7YNkIFAF75ylduqVjhk/T1YH5+XjwMbCViRcA0hvNpPtOlgGVx02g0pNWLooWtQY7jSGsVn9ZHIhF56s+Nu5n4xfOigbzdbosYiMViMgfFjAGORCIIhUKIRCIym6XX66HRaEhrE6fdU4hwAKLH45E5IJ1OB47jwHVdZDIZOXea1ekzYbqV2d5G/wWrSzS+U7zZto1arYZarYYzZ85gfn4ehUJBzsGcV0IfCq9ZIpFAIpFALBaTlLBGoyGfxVY3ng8AOSemhVEIpVIpqY54vV4JZ6CXyBSVrOTEYjEZ3smp9xzkGA6H5f6wMmX+3XEcuT/pdBozMzM64FFRFEVRlOuKbSNUPvKRj+C9733vlg5+rFaraDab63IsxhOzTYtChF4EPl2nsZtP4oFVE7457I9xvzR4A6tT7v1+P/x+PxqNBsrlMmq1Gur1unhC1gowGrBte3mYEqs6nE/CzTM37my/cl0X1WoVwHKil+M4qFQqKBQKA/HLZhIWxQHPk1WCbDaLUCiEcDg84H9h+hfPyZxhYgo3muLZ6kVBUKvVsLS0BGA1KCCdTg/4U3jtGKsci8XkujKhi9eG14MCpNfroVarodFoIBqNolKpwHEcaQnjGtmuZ7aJlUolZLNZSVlzXVc8LxSwruuiVqvJMc14alaGzBjpSCSCTCYDAOv23VUURVGUjaZ3+DgO/LfsVi/jhiXz6Pb3pwDbSKgAQKlUwoc+9KEta2M5evSoVAmullgsJvNB6DUAIB4VYLWNiX/nBtdxHDQaDTFI8+k6/RAUBUyGMtO5+v2+xPFygKX53na7LZURChNOXaeoYutTq9WSagbXC0CEl23bkk4WDAZlw833cuNfLpdFuJgmdtu2ByJ/g8GgGPFZQWCbVSgUQiKRkMnrwGoVirNYKBQqlYpUqvj5vA8AYNs2wuEwQqEQut2uRP/yXlA0MKXLbJPrdDqIx+MIhUKwbRuxWAw+n28g3pptbPTPtFotlMtlNJtNEUJsj2MLW7vdHpibwypYNBqVNTmOI/ecQy7T6TROnz4t82kURVEURVGei5Fv+ZD52ENbvYzLYlsJlfe85z14/etfj/Hx8S1pAXvooYcQi8Wwa9euqz4W52FEo1ExUtOLwo0/Z2qYT9P5Gj6BZ9sPTe8UIIwxpkghfNrPNjNWQkzTOTfH9Gy4riumdgokABJ/3G63xVNCkUKzOqOUKQy4eWcrEz03FJ+sFNG/AUCGSfIz+fk+n08qRpZlodVqoVqtolwuw3Ec+Hw+FIvFgVYxtrQ5jiPXhQZ6r9c74P0wh4xy/RQ7vCedTkc8Klxfp9NBvV6H4zgiLHhNWPHhsVqtFprNJorFIrLZrAiqXq+HSqUiFSqeA8UTP7/RaMi5MUWNFa9wOAy/34+FhQWUy+Wr/s4qiqIoinL9Y/VdYAuTdq+EbSVU2u02vvWtb+FDH/qQDAjcTHK5HEZGRtYl+evIkSNYWlqStCxgNWnL5/PJJpObcT7N93q90nYErJrxaeymd4IzNoDBuR2m0ZwVkrWGd26EOSiRVRBu5GmwZ2tZOByWNbEly5wJwxamZrM54Acx44H5eWblhe1LrFpQuLGli8lZ/LtZjeJsFNu2kclkRMiZFSxu6lkxYsWCpnW2UXGtvI5s9zJjhUOhkKR+WZaFdDotog3AwAwaRiazgsRYZWC50hYKhTA7O4tKpSKfa9u2pJxxwCNn2ZiCkLNjwuEwhoaGcPjwYZ1CryiKoijKZTH+VS+SH//OVi/jstlWQgUAfv7nfx6xWAw//MM/LBv8zYKbWj7Rvxr6/T5KpZIMP+SGlxtybuxpbqdx3NzUNptNqQrQ4xEOh5FMJmHbtlRezKfwZsWAput6vS7Vl2azKRUX+lro1aDwabVaiMfjYhznZ7Mq1Ov1EI1GkclkBnwobNMCVj06tVrtoonrNLR3u10kEgm4rivBAhRXrE6wmsLYZa4/Go1KxahUKslgTV67tXHHrLbQB0NjOitLbCFj1YkVDoqntfNWOHyR72MYAc3uXFu9Xkcul5PIZlZmWCkzv3usYDEkodlsIhqNSgue1+tFOByGz+cToWTOplEURVGUa4nu2Qu49feKW72MG4bJf/Qg8ekn4HaunX3DthMq5XIZ/+W//Bf86I/+6JZUVarVKhqNxrocK5fLibBg+xBTuILBIMLhMCKRiMxE4QbX9JuYJm3T/M6NvSmq6O8w/SHAqhGbLWA0vPNpPqswnU5H1siNPM3u3PBTZDDml61rPDbXymoLN+axWAzdbnfAuE8fCqsQFEZmpLEp4tgWxooPhUi1WhXBxNY5Xpt4PD4gRihkuMFnIpvZysY1MYWL4pJVL94HCgtWcEwvEkVZpVKRygmrPWzlK5fLUjXhNeN9N5PaGMJA8RmNRjE2NoZvf/vbmJ2dXZfvqqIoiqJsOv0erHJ1q1dxw+Bt9tG/xoJ3tp1QAYCvf/3rmJmZwS//8i9jcnJyUz/7zJkz8Pl8GBsbu+pjzc3NoV6vi/maxnG2FHHDbrb38GfcbNPHYfoqzE0+Y4Hp82DCVrFYRLlcRr1eR71eR6vVkk08KwX8Gc3srCKwwkLzdzQaRSgUEmFDwdLtdgc8GhQwbMviv3u9HiKRiLRORSKRgSn2tm1LNYdijYlobBFjG5hpWPf7/TLFndeAM0kY8wusVld8Pp9Ujni9eO35HooNChuzcmVWT8xZK2bFifeNry8Wi3AcR0RbvV5HNptFoVCQawRA7guwKoAAyBR6Xgufz4dMJiN+HdOfpCiKoiiKcikm/9GD8LePbfUyrphtKVQajQZ+8zd/E7fccgve+MY3bmpl5cknn0QkEsHtt99+1ccqFAriNeAmlJgVCHMQIisHFDPmU3o+fefmmhtfcz4HoQihT4LChhG+rKBwMjvbwTiAEIDMECEUQjSUc+PO9VEsUGgBqyZx+jlYxWi321JpMtvbKGRYxTBb48zrRpFjWRYcx0G73YZt2xJeQDHFuSy8Zub8GlO4UZzwGpox0BSD/J0Z7UzPCT00rBox6YtpYGxna7fbcs94T1kNopjh/afPiGLM6/UinU4jFovhkUceQaFQuOrvqKIoiqJsJb18Efs/dm095b8WCRXa6K20rV9LbEuhAgAzMzP41Kc+hTe+8Y3YuXPnpn1uNpvF2NgY9u3bd9XH6na7WFpako0mzeBsf6IYMeecsFUKgDz95+BGGsZZueAGvN1uy7EpZBzHkbkwHADZarUGkrvoWaEhnMKAXhSuiZtqxvoyRrjRaIg/Ze3mmkZ5CiEa6WmmB1Zb0the5ff7JbKYx+NaOICS72HIQDgcFiFAWL3itaR4MVvq+HuztY7XwRSWpleG947xz/TZsOJihggUi0XMz89LWxorVIVCQUzzjuOIV4VijNeQ14ffCd7zyclJnD9/HgsLC1pNURRFUa553E4b1neOYN//6Tz/i5UXxI4ve+B/5uxWL+MFsW2FCgB86lOfwsLCAt7xjnfg5ptv3pTP7Ha7Mql8PSiVSuh0OrJJNyN/TT8JsCpSKGDoZ2FVgUMYOR3e9E4AGGhRoufFNJdzlgf9Kq1WS6or9GSYlR8z0co0m5szSihuKLjMv1NoUNRQIDWbTdi2LeKJVRya1ik8zA0613OpSfWs/FAUEV4fVl7Mn7PCQXHBv/OzzSoJqxzhcFiM9hSNDAvg2piAls/nUSqVACwHIHi9XlSrVRSLRfFAUVzRM9Tv92U+jlml4j+Tk5OIRCI4efLkuvmoFEVRFGWrcbtdeB8/vtXLuC7Z8RUvEl89gV7+2uzC2NZCZWFhAR/96Edx880343Wvex1GRkY25XOZhLUenD17FuVyGbFYDNFoVCKC2fKztiUMWN2cso2IG3N6I0zzNYWIOXWdgoeeElY86Fcx285YfWGUsDmFnaKEQoBmdH4Of8Y2NFZSgsHgQEsYADGiU8B4vV7UajXxwVDAmHNegsGgCAWz/YqigFUQiihWiwDIOTMJjcdg6xlb7iguKFTYDkYRw5/xXjGFzBQ/Zgtfp9PB0tIS8vm8eHIobKrVqpj42XJmxhizysb4Z7NtLhwOY8eOHXjssceQzWal6qIoiqIo1wNuu43pT2/1Kq4/EoeK16xIAba5UAGAxx57DF/5yldw77334rbbbtuUQZCnTp1CqVRCJpO56mMVi0XkcjkEg0HEYjEAkKf25kaem2qzDYyD/cxNM4CB+ShrW5q4gW2323AcB47jyAwRAFJlMVvGyuWyVHXMTThFBSsebEsyRRHFEzfp5jR5v9+PcDgMAPJvijTTJN/r9aTNjefJ11GkUditnW9CUUQhUqvVRFSY18ccJEmBYQo7fhaFCQULY437/b5Uo1g1ouBjAAGrQrlcDqVSSTw9nU4HjuNIyxcraWYqGme38BqYn+fz+TA0NIRms4kTJ04MDKpUFEVRlOsBt9tF6P4nt3oZyjZj2wsVAPjkJz+JpaUlfM/3fM+6eEeej0cffRSlUgm33HLLuhyvVCqJUZ2bYQADU+XNBC1WHsxNNjfGbDkifCpPrwU3/5xqvta4TTN3o9GQqej0SLC6Ycbksrphigdu3PnEn0LFrEZQ6FAgsX3NrGjQEM9jsTJjTomniDDPwWzj8ng84mvx+/2o1+siLgDI8SiyeJ0opsyAAIoCrpX3ir4U3icKFFZ3GJFcqVSwtLSEarUKy7IQi8UQDoclocscbGl6hZrNJgKBgIglroOVs3g8jmg0iscff1xnpiiKoijXLW6vh7FveDH2De/zv1h5XtKP+WDlS1u9jKvimhAq58+fx9/93d9hcnISr3vd6zA8PLyhn3fmzBm4rou9e/euy/GKxSLq9TpCoZAM7OOGlXDjav6MG2uzZcrc7AOrhmtWYkzjN9O+arWaJHH1ej3UajU0Go2B4ZL1eh3FYnGgWmAKELYnAZANOtfFzzf/TbHAzTbnntDjAay2uFFEUNy0Wq2BigqvS7PZFFFmwnNlpYWDF81KFAWX2TJmekQomEyfEEULj8/z5XvNxLRSqYSFhQUUCgURF5FIRFLJKpXKQNWK6zQrURRwZrUqEAhg7969OH36NE6cODHQdqcoiqIo1xX9HuJ/+RDif/nQVq/kmif5lB+jnzqJ7sLiVi/lqrgmhAoAfOtb38K3v/1t3HbbbXjZy1428FR9I+AwxvVgaWkJS0tLCAQCklxl+iTMOSpmaxs364TVBXPSPDfTZjsUxQoAOI6DYrEopv1eryctYRza6PV6ZYo6Z4KwhYvXmQMHuZ5LVV34c1P8cOPdarVksKRZFVnrx6FICgQCYkIHMFARoXAyzfX0n9AEv1aE8M+8hlwX2+B4DYHV9jd+rhm7DGAgoatarUogQaFQkDXE43H4/X7UajVUKpUBscjz53Uzq0hmFHMgEEAqlUIoFMJjjz2mIkVRFEW5YUg+5d/qJVyzxI/4Mf6XR9DL5bZ6KVfNNSNUAOAf/uEfUCgU8IpXvALT09Mb+lncXJqT318orVYLi4uLaLVaCIfDspleW5Gg/8P0PrDVClhtCePTfbPqYAoWswWLZvlmsykb5Xa7LU/+OTwSgEya50afHol2uy1VFAoWVmPMae8UNmbFgoKC1Ov1AcFDgeXz+cRwn0qlkEgkRGwBGBAf/X4f4XBYPDyBQEDmmlDY8N8UdKxI8XfmRHr+jILJ9ImYbXZmlYjXhEM1GdnMOTD83jCGmCltvEZcEyfWm5HJNM8nEglkMhk8+uijKlIURVGUG4qRP3oAseN+wN3YB9PXE9GTfsSO+7HjY0+jVyxu9XLWhWtKqORyOXz961/H6Ogo3va2t62L2f3ZOH36NMrlMm666aZ1Od7i4iKKxSJs25bhfWZrEisCZnWBngaa4VmZ4Maf5nJupvl0nqKET+YpVhhlHAqF0Gw2pR2pVCqhXC4DgKRTsdWKsLrBQZKmT4Xix4zzZRWCYioajUolJB6PA8BFm3dGOLMaQSFkVp5MTw9bykyxQ+HEpC3+DsCAj4bH5FwaCioztIBtX6aXhSKS7Vz8XSAQQCKRQCQSkZS0arWKUqmEYrEo19oUW+YQS95XnqdlWZiYmECpVMKTTz4pFR5FURRFuVEY/90HAP2/v8sifNaPid9/BOO/+wD6Kx7k64FrSqgAwBe/+EXcf//9uOOOO/D93//9G5YCNjMzg1arhbvuumtdjjc/P498Po9IJILh4WGpqpgDB80BiGZbFZ/eA5BNbLfbFfHSbDbRaDTkd2s35Pw9sJwuxc+vVCoDlQG+ju1hpqkfWPXB0ANjemHWCgK2pZmtYGwxAyDnGwqFpLLDqoLjOKjX62g2myKWuBYKCQYAmJWdtfNMTFM808EoNHidKXgogHh+XCMFBOecdDodVKtVFAoFNBoNJBIJ8c3wfX6/H71eD5VKRUz0ZhWGn0MhZVbBuI5UKoVYLIYvf/nLKlIURVGUG5bQgk+rKs9BcMGP4IIfk//1QbjX4SDoa06oAMti5YknnsArX/lK7NixY0M+gy1FY2Nj63ZMxtMyttcc3mhulGk6N5/Wd7tdVCoVqQJQHJjeDjPimELCjCXmppneB4oCChB6Svr9PorFoky1Z4sSBQs9HmwVAyCCw/TVmMb4RqMhE+NrtRqCwSDC4bCkdbGq0G63USqVRBgAGPCYULix2kHBRwM7abfbiMfjUrXi/BcKHbZmUfAxHpgtYvwz74vf70ez2UQul0M2m0UymUQ6nZbqGNdOMVQqlVAqlVCr1QbEEO8fWwDN4Y88p2QyiYmJCTz++OPr9t1TFEVRlGuRne9/AFZXhcolcS1M/8YDmP6NB4Dr9KHmNSlUCoUCHnjgAXg8Hrz73e9eFx/JpaAQWC9OnDiB8+fPw+fziVG/1+tJOxaAAVM6oXeD/5j+k1AoJIJkrZ/CHMJIAdBqteD1epFMJhGJRFCv11GpVBCJRBAOhweikJlWxgGNFBVmGhcFEzfiNLZTyPj9follpoih4DE9ORx0yGvC8+dnsC2Ok+jNFqlmsymCDVidM0PzezQalahkigm207GyQqHHtbD6wxYyx3HwzDPP4MyZM/D5fLBtG6lUCsCy2OH8Foq8XC6HxcVFqfyYniSeH8UahYzX60U4HMbk5CQKhQK+853vrNt3T1EURVGuVXyOB96qVysra/A61+Q2/oq4Zs/wW9/6Fj7xiU9g3759ePOb37whKWCLi4s4d+7cgKn7amg0GigWi/B4PDKpngKl3W4PtDqZVQkmZXEzSwHCSkQoFBJxQTFjti+ZYqLZbCKfz8N1XezYsQPJZBK1Wg1erxfxeBzpdFp8Il6vF41GQ1rEGKNrtkzxH9NLwuhjDqtktYEiiH+ORCLi3eBxzbYtM4ULgFQ5TLFDXwoTuNh2Zdu2rInX1jTQc2ZMr9cb8LmY58O1l0olnD59GseOHYNt20gmk0ilUvD5fJidncWJEydQKpVEjDCumJ/N+wVAhC8rXRRdvG9DQ0MIhUL41Kc+tS7fOUVRFEW51tn97x7E3n/7EAJLXljN5X9uZNFiNb3w1L3Y+2+v/xhn3/O/ZPvywAMPYHh4GG9605vwjW98A3Nzc+t6/BMnTiASieD1r389PvvZz67LMdn+FY1GkUgkZFPPJ/5sI2LlhB4UbtDp7Wg2mxIvzEqHWQVgIhY9Jnx/oVCQzbrrukgkEvKUv1wuIxqNAoB8BgCZX1Kr1UQs0DDv8/lk3gcrGBRJ3LizYtFut2HbNlzXRaVSkUqL3++H3+9Ho9EQI3qz2ZS2LrMVi59rijZWlur1ugx9DIfD6HQ6sG1bqihsJ2M7HTA494WxwRR2jUYDs7OzOH/+PHq9Hg4cOICRkRFEo1HU63Xk83mcOHEC0WgU8Xgc/X4fS0tLmJ+fl2oUhQkrRLw3FHUej0c8Q7ZtY2pqCp/4xCfW5bumKIqiKNcTu379Qfnz3K+8HM7eLmBdny1Pz4a3emMIFHLNVlSA5VSlgwcPIpvN4jd+4zfWvapSLpfR7Xaxf//+dTvmzMwMTpw4gU6nIwMBWT1gZYCeFHokzKhibm5Ng3Wz2RSTOl9HkRAMBmHbtsxvqdVq4lVpNpsihMrlMorFIiYmJvCWt7wFP/ETP4HXve51iMVi6Pf7cBwH7XZb5oHwfWbLFdfMKhHvh9frlcoBzerhcFg8GfTtmFHMa1vgKFj6/b74ZiiASqXSRQliFC2NRmMgatj09/DnPC4rToVCARcuXMDhw4fx5JNPolqtYvfu3RgfH5f2rgsXLuCJJ54Qv0u/30ehUJCZOe12G61WS+4DW/N4PqzwsHpDsXXs2DEUr5NIQUVRFEXZKHb8fw8gfuQGMtq7Fnxl3w0lUoBrXKgAwNGjR/E3f/M3eOlLX4qf/dmfXffjc0jjepLP51Gr1RCPxzE8PIxwOAxgcPYHcayUWQAALm1JREFUI385/DAQCCAYDIpnwrZtqRhwjojpVTFbtSg0lpaWAEDiiGOxGIBlDwfboR5++GHEYjF4PB7s27cPP/MzP4PXv/71UmlhpYAiAVgVJBRQFEtmjDIrRRRU9NhQJNB0z009sNwqx6ABbuh57hQM5ufati3tX7yepnHe9OswMYzvbbVaaDQaKJfLmJ2dxUMPPYSjR48iHA7jwIED4uEJBoNYWlrC6dOnJZSAvpxCoYB8Po9Wq4VarQZgNQGNrXy8LxR5lmUhHA5jbGwMgUAA3/jGN9b1u6YoiqIo1ytjf/AAUk94t3oZG4trITTvx/6ffQR7/n8PPv/rrzOu6dYvcvDgQfzKr/wKfu3Xfg1f+cpXcPz48XU7dqFQwMzMzLodDwDOnDmDSCSCu+66C/F4HLlcTtqmgNWn7Zwa7/V6L5pGz3YuM6KXgocb47VGdG7UO50OFhYWEA6HkUqlkEwmUS6XEY/HUSwW8bd/+7d4xzveIUJj//792L9/P0qlEv7xH/8R5XJZWswASBWk1WohEomg2+1KNaFeryMej6Pb7UpbV71eRyQSkZYstmmxGsKEMB6f50lvCkVcMBgciGkGIOdptprxdRzgyXV0Oh04joNer4darYaFhQUsLi6iWq2i3+9jcnISO3bsQDQaRTAYlCn0J0+elHQuTqzPZrOYm5uT6pY5+JKpaRyAWa/XRahFIhEkk0nYto3/+3//77p+zxRFURTlemf4ww/C+67vwtJ91180b+y4f3mWzA3MNV9RAZY3yEeOHMFTTz2F3/3d313XYx8/fhwPPfQQbr/99nU9brFYRK1WQyKREAM7jejmkEE+iefvAUjcL39PszaFDMWNWcHgQEVWIXq9Hubm5lAqleD3+5FMJuHxeBCPx3H06FF88pOfvGgaejKZxNve9jZ83/d9H4aGhtBqtVAsFmUGi9nKxM291+tFKBRCsViU6g7PoVarifndFA0AZOAkN/ihUEhimJvNprSK9ft9RCIRaQ0zJ8x3u12ZDM+ULSZ4zc/Po1Kp4MKFCzh06BCeeeYZPP300zh//jxs28aBAwcwOTmJaDSKQCAAj8eDubk5ZLNZhMNhTExMyDpzuRzOnDkjAx4ZD801s3LE+2B6hoaGhjAyMqIiRVEURVFeIOn//SDGv3p9VVaST6lIAa6TigoAzM7O4oMf/CD++q//Gh/60IfwC7/wC+tyXLYfTU9P45lnnlmXYwLA3NwcTp06hWQyiVgshmw2K0/g2brFOFt6LoBVg3q73UY4HB4YFslWJk6E52beTOli1YLVimw2i9HRUTFzZ7NZDA8P48iRI5iZmcF73/vei+Kf9+7diz179uCpp57CU089JWllXq9XDOt+vx+O48iARVYxWGkBMOAp8ng8sG0bACSyl+fOwIF+vy/tV5xzQ9N9o9GQlC4KNYoDirVarYZGo4FmsykG+YWFBSwsLCAUCiGVSmHHjh0YGRlBPB6X6OhqtYpisYi5uTlJ/KKoO3PmDE6fPi0tX7yu5hBJCiev1wvbtkWkZDIZJJNJPPTQjdVvqiiKoijrTexvHoKv9VKcf8O1Z66/6c+78Hzria1exrbkuhEqAHD27Fn8i3/xL/CP//iP+NM//dN1m0NhTldfT0qlEorFIpLJJIaHh1Gv12VTy42uORWeFRHO/GCrFTfrAKTiQNgCRlHAzTQ/g61tY2NjiMViKBaLCAaDSKfTKBQK+MhHPoJ3vvOdSCQSA2u3LAt33HEHpqamcPjwYZw6dQqu64rRnW1O/Fk4HEar1UIoFJKBjYFAAI1GQyoWPp9PvCpscfP7/dLq1mq1xCTP9C5WVfj+UCgkIiYQCKBSqSCfz0vFhkMsmRBn2zZ27tyJdDqNeDwu6WsUc8ViEbOzs+h2uxgbG5M2O8uyUKlUcPLkSRSLRWk1o0hiIALN/BRnABCJRBCPxzEyMoJDhw7h0KFD6/7dUhRFUZQbDfvTj2JX8x6cefNWr+TyuOWPqnCPzMDtdq7bgY1Xi+VugwtjWeuXLZdOp/GHf/iH2LVrF17xilesyzFjsRgymQzOnDmzLscjlmVh7969uO+++9DpdPDMM89IdDE3vWyXYpWi1+sNeFFMQzl9D/R+sMUIWJ3uTrM9n/bTaxEOh8UvwQ1/t9tFPB5Hq9XC6Ogo3vGOd2B4ePii8+h0Ojh//jyefvppVKtV8ZtQbMRiMTG5m4MqLctCtVqV8+VcGB7T4/HIufIadDqdgVQ0xhZzDkskEkGxWESn05H5J7lcDrZtY2RkRN5PkRMIBGSIJE347XZbTPyLi4twXRepVArhcBi5XA7xeBz1eh3Hjx/HmTNn5HwoyFjh4jXmfQoEAkgkEkilUojFYqhUKvjCF74wMNxzs3HdGyUuRVEU5fmJW2n3ZdZrtnoZylVg+XywAgH077gJx98V3OrlXMSB/zSLfrEEAOg3GipQADzsfgUVt3DJ/ch1J1Qsy8LExAQ+/elP49FHH12XJDC2IG3EhnJsbAz33XcfxsfHcfz4cZw/f1424mzn4jwV27Zlc82qCc3krDAwRYsVAfo/6GEBMDCXhD9jstm+ffswMjKCWCwm7WZerxeVSgXlchl79uzBm970Jtx6662SztXv91GtVnH69GnMzMzI/BBu1OkhaTabiMfjaDabIgyq1aoMrOQ8GFYsgMHBl4wkpghgZYVhAdVqFcCyr6hWqyGbzWJkZAQveclLMD09Da/Xi7m5OTkGje+8Hp1OB+VyWbwyrVZLPCpmhaZcLmNmZgbZbFba0lg94jVnchuvs23bmJycxOjoKFzXxezsLO6//34JItgqVKgoiqKsokLl+sHy+eBZSTfFUApH/m1mS9Zx6787OfD3Xqmk4mQNzyVUnrf1y7KsKQD/B8AoABfAn7iu+weWZf1HAD8DILfy0l9zXffzK+/59wB+CkAPwC+4rvvFqz6Ly8R1XczPz+Pd7343PvnJT+Luu+/GY489dtXHDIVCGBkZwdmzZ9dppcvkcjkcOXJE0rfy+fzAkEBWVPx+v1QduNF2HEc2xubGvt1ui1ghbEkCIK/n61i9abVamJ+fR7VaxY4dOxAIBCSRy7IsRKNRnDx5Eu9///sRiURw880344477kA6nZZWNFYOKpXKQAsW18W2L27igVUPCT0qFF1sc2P1wxQCDBRg+1y5XEatVpPZN6FQCPfeey9e/vKX49Zbb5Xq1NDQEBYWFlCpVMTDwkpNvV4Xj00gEEA6nZa10tSfz+dx9OhR8d+YwzVZhbJtWyo+vFfBYFAGbZ46dQrf/OY3t1ykKIqiXEtca/sRZWtxu130OJesXMGB/1gHggEc/vejm/DhFg68/xwAoKuz0a6Ky/GodAH8G9d1v2NZVgzAY5Zl3b/yu993XfcD5ostyzoA4G0AbgOwA8CXLcva77ruYITUBtLr9XDkyBF8+ctfxgc/+EF893d/91UfMxQKYXx8fN2FCifCFwoFJBIJJBIJ5PN5AKvGecbYsvpQq9UGJrJzw2xOqWfrFzfMbLkifNJPrwhFAzf75XIZQ0NDGB0dFfM+k8Ho93j66acxMzODZDIpLWM0oJsVCVZY+Jlmm1o8HkepVEIwGJTqkTkdnkKg1WpJi1elUkGpVEK1WkUul8PS0pKkf/l8Prz4xS/G/v378aIXvUjEx8jIyMB8F17DarWKarUqc00ikQhisZi0rzFhrNPp4OTJk5idnZXhlxxkyZY2Rg8Dq61qFIOtVgvdbhenT5/Gt7/9bTQajXX9HimKotwAXHP7EWWb0O+hO78AWBYO/DcLsCwc/tWLW9mvlNCsH3v+7+zFv3Dd5c9TrprnFSqu684DmF/5c9WyrCMAJp7jLW8C8Neu67YAnLYs6ySAlwLY1Ck1jUYDv/Vbv4WPf/zj+KVf+iX8/u///lUdj/MzNoKlpSU888wzuPfeezE2NiYbZ2A5lpiCgr4PYNUUz7YwPvWnEdyElRRuntk6RvM6qyk0iAcCARQKBXS7XTiOg2AwKGlbrusiEokgkUiI2KhWq6jX6wgGg7BtG4VCQUQD27GY+sUqkFktoRhgZYPCgMKFwohzZVzXheM4Itzo+YhGo7j33nsxMTGBdruNc+fOodFoiIhimlc8Hke/38f8/LykhQUCAfHNMBqZVZp+v4+lpSXkcjnU63VZt1nF8vl8qNVqch/MYZeBQABDQ0NYXFzEU089JcMgFUVRlMvnWt2PKNsI10X39PID51v/IHDRr4/+bApu8NJt/sMP+jD0SH7gZ1a9ie6Zc+u/TkW4otQvy7J2AbgLwMMAXgHgX1uW9RMADmL5KUcRy//RMPNWL+C5/0OyIbiui9OnT+N973sf/vt//++4//77rypeuN1uo1AorOMKV+l2u8hms7hw4QJuuukm2LaNSqUim3IAF7Vy9ft9RKNRaQ8jNNwDqy1ebKNiJYPJYgDEo8Ip96YRnnNBvF4vksnkQPRvIBBAPB6X+SX1eh21Wg3NZhONRgO9Xg/RaBR+vx+1Wk18NEws42bdcRxJ40okEpLqxSGMbBdjZSIYDMr0+ng8jlAoJNWuZDKJvXv3olgsol6vI5/PIxQKodVqoVKpIBqNolQq4emnn8bc3JyIJnp7KPgcx8Hi4iJKpZIIpmq1KqKmUqkgmUxKu1gkEpH7wyoWDfvRaBSZTAZLS0s4efIkKpXKhnyHFEVRbiSupf2Isj3pHb54OPjNH7sVru/SIwa9s0voLixu9LKUNVy2ULEsKwrg7wC8z3XdimVZfwzgP2O5T/Q/A/hdAP/yCo73bgDvvrLlXhmu6+LBBx/Ed77zHXzgAx/AG97whoFN/ZUSDAaxZ8+eDYmTrdfrOHv2LCYnJ5FKpVAsFgd8GvTJdLtdmWxuDm9kYhXnlPApP1vCWq2WRP0ylcr0WFAEsU2JlRka30ulklQSwuEwXNeVKgTng1CksCKUTqcxMjIiFQqKmmAwKBWYRqOBfr+Per0u0cJmJYXnZq6TVaFgMIjh4WFMTEwglUqJcGi1WgCA0dFRDA8PI51Oo1wu47HHHsPc3BzOnTs3UJ1pt9vodrtotVoyIHJhYQG1Wk38M3wNrznN/LVaDcFgUFreHMeRpDLbtjE+Po5cLoeTJ0+iVCqt+/dGURTlRmMj9yMhhNd/wco1Q//JI8/6u+tv7v21wWUJFcuy/Fj+j8JfuK77SQBwXXfR+P1HAfzDyl9nAUwZb59c+dkAruv+CYA/WXn/hsUf9Ho9fOhDH8JnP/tZvPOd78Sf/dmfveD0rkAggPHx8Q0RKr1eD4VCAefOncPu3buRSqVQrVYHKiH0TLCiYKaCMSWMIoSVAh6bG362ivHJPwDxbZhiJRAIwHEc+R1FAAUSBQVngoTDYcRiMcRiMdi2jVqtJmvi4Eoa0hk9TB8Lqzz0k3i9XjkXvpbnw/cHAgHYto1MJiM+EVZcJicnUa1W4fV6cf78eZRKJZw7dw7ZbBblchmtVmugFY2DIPnnXq+HUqmEfr8Px3FkRo0pklg1oZGe/hnz+g0PDyOXy+H48eMqUhRFUdaBjd6PxK20xjEpyjbiclK/LAAfA3DEdd3fM34+vtIvCgBvBsC+qs8A+EvLsn4Py+a1fQAeWddVXyFPPPEE3v/+9+M973kPHn30URw+fPgFiZVms4mZmRmEw2ExTa8nzWYTJ0+eRCQSwY4dOzAzMyOeCcINMlugLrVJZqIWjfTAaosXf2dWY7rdrmzcvV6vVBFMQzkFCqOImbpl2zZSqRSGh4fh9/th2zbC4bBUR1hB4WexrYyfQ+M/z7/VaqHX6yG2EinI4Y98LdvCOADT6/WiXq8jFouJMMvlcpiZmUEwGMTx48cRjUaRz+dlHRRelUoFS0tLIl7q9bqIKba5sYpCscRrw/hlVlMCgYCY/9mKVigUcOzYMRUpiqIo68D1sB9RFOXKuJyKyisA/DiApy3LemLlZ78G4Mcsy7oTy6XWMwD+FQC4rnvIsqyPAziM5UrZe7ZDwsb//J//E295y1vw0z/90/jVX/1VaQ+6ElqtFubm5nD77bfj4MGD677GXq+HfD6PI0eO4J577sHQ0BBKpRLK5bIY3z0ez4Cnot1uD0xu56afXpBLRRRz48/ftdttmXrPVC7TE8MNOYUKN+9MtWo0GuLV4OBIzm8plUpwXRfhcFiGK3KTz8/hPBbGDvNYrCSZccv8fFZjmJBGUdTv95HP5zE7O4t4PI5arQbLstBoNETUlEolNJtNlEolOI4j0+OZzEXRwWvInwGQBDSzRY6fzerR2NgYyuUyjh49inK5vO7fE0VRlBuU62I/oijK5XM5qV/fAnCpISyff473/BaA37qKdW0IH/zgB/Gbv/mbeMMb3oDPfe5z8nT8SvD7/ZiamtoQoQIsb9YXFhZw5MgR3HXXXWi322Jo51wSy7JEaK01yfv9/ovaulzXlXYqVmhMXwqwLGJYTWClBoAch8lja2e8dLtd8W84jiMpV2zTos9jx44diEQiEp/cbrcRCoVEMHFd3PibXiIztYzrpuhwHAf9fh+zs8vVfCZusfLi9XpRKBRQrVYl1jifz4v/hf+sFWAALprbQuECQKorZjsdq0qVSgVPP/20JJ4piqIoV8/1tB9RFOXyuKLUr2udL37xi3jxi1+MX/mVX8GRI0dw8uRJSci6XNrtNk6dOiVVjY2g2+1iZmYGw8PDGB8fR7lcHhhOSBM4258IRQY326xYuK4rnhAenz4Xel64wY5GowOtZiasYphtZDw+xQUFC6fesxIUj8cl+YvCg8ekQKDQ4hwW/sy2bXkdhRhbx1gd4nHZ1gVAqiazs7PymlqtJt4bU2Tw2prrYfWK1RNeW2DVr0IxE4lEMDQ0hFwuhyeeeGJDWgMVRVEURVFuJC6dwXYd8zu/8zvweDz40R/9UUQikSt+f6vVwuHDh3HzzTdvwOpW6XQ6eOihh+A4DsbHxwfasJgCBqx6TjgB3Xzib7ZwUVRwA86qB9O7AEj7Eo/F3/OYbINiBYfCiC1obJ3i+judDprNJmZnZ3Hu3DnkcjnUajXk83l5DxPJaNRnGxUrHlwzf0aR02q1RDS0Wi20Wi0RLuVyGXNzczh//jxOnjyJmZkZnD17FktLS3AcRxK+arUa6vW6fBavAafTm9Ub85rzPtArxDkuS0tLeOyxx1SkKIqiKIqirAM3VEWFfPCDH8Sv//qv45vf/CYeeOCBK/ar2LaNN7zhDTh8+PAGrXCZbreLb37zm/iBH/gBNJtNzM/Py0R0j8eDUCiERqMhrVRsz+JTfgADLV6m38JsuwIgfzbnqFCgsC2KxzSDCPg7wvYxrpHViUqlgk6nI/NYRkdHxX8SDAalHa3RaIgnhsdotVpy3Gq1KqIiEAhIpDAASesqFosyuLJYLIqXxufzDZwX/2yGC1AYhUIhSR0zW96CwaBMoOewyX6/j2w2i6NHj25YlU1RFEVRFOVG44YUKn/zN3+D++67D7/+67+OX/zFX8SRI0euaIPZ6/WQy+U2cIWr1Go1nDhxAi960YtkOCKrGJxfQjFiCguKBAoMekCYuMXziEQi4mlh5SUYDMpEerZwEQoTHt9s4zIrNsCqSOJ7FhYWUCqV0O12UalUZC4MjfaVSgXdbhfRaFRigoHlGTMUI8Cy4Go0GiK2qtUqbNuWwY7VahWWZcm8F9N3wnWxFcyMPubr+J61/hxWm+LxOLrdLsbGxhAOh/GVr3xFqyiKoiiKoijrzA0pVADgl37pl/Dtb38bP/RDP4QPf/jDyGazl/3eZrOJL3/5y7Bte2ADvRG4rovHHnsM+/btw0033YSzZ8+i0WiISZ0VAcbk0vDNjTZbw4BVP4g5o6TZbCIajUpiGKsObAljpYWbeg6WBDBQSTEjjnl8mv858JFtV81mU37n9/tlVgnXzjYyDoNsNBoD82I44yUcDsvncCCk6WVxHEfilVmdCQQCIrK4bl43emrY+hUKhQZS1SjAIpEIQqEQYrEYPv3pT1/VEFFFURRFURTl0txwHhWT3/md38EP//AP48CBAwNVg+eDrT6vfOUrN3B1q7iui7/6q79Cu93G5OQk/H7/QFyuWU2hp4QtXfwdU78oJtjuRiHh9/sRDAZF+NBsT98HxYqZHnapdfK9bCPjuihSAMjQxnK5jFKphKWlJeRyOZRKJWSzWeTzeWSzWeRyOREpnHbf6/UkTpiCyufzycBGChpGCLuui2QyCdu20e12pbUMWE4T42wWVk0ASKXG9KswbS0QCGD37t1wHAd///d/ryJFURRFURRlg7ihhcqnP/1pfP3rX8cv/dIv4c4775RN9eVgWZZMLN8sPvGJT8C2bUxPT8O2bama+P1+aQczE768Xq8IE0YIc2NNocEkLv6cXhDbtgcm1Zu+kGAwKAMnCT+TlZpGoyFroKGfn8W2LoqlRqOBSqUCx3FktgmHLzqOg1arNTAYkudJE329XpeIY86aaTQakjhmenbo5+Fx2A4HQAQOX8ekr3q9jng8jlQqhampKXzxi1/EE088sTE3WVEURVEURQFwgwsVAHjve98Lj8eDN7/5zZiamrrs93W7XTz44IMDHo3N4H//7/+NoaEh3HTTTQiHwwAwMM9krdiiNyMUCiEcDotXheKEXg2zbYptVNz09/t9+RmwOkySrVI0mFOMmK1iXq9XqjbActucbdsXCR3TS8I0MADSrkWhYgoLpoyxDY7H8Xg8iEQi0sZltrSZ58tKEc8RWBZwrMYAy+b5ZDKJ6elpxONxfOELX8D8/DwURVEURVGUjeWGFyoA8Nu//dv43u/9XrzkJS+5ZEvTpXBdF8ViEW94wxs2eHUX80d/9EfweDyYmJgQ8QEsVzvYdkUBxY07Z4FQiLA9bO3GnBUFigb6M0yfBluvKIpoxqdQor+DHg+PxyNihv4VekJs20YsFgMA+TsrMcDq7Ba2gJnnCgwOpGR1yTwfznhhAACrOGbwAK8D/+F7w+EwxsbGsHPnTnzxi1/El770pY26pYqiKIqiKMoaVKgA+Na3voWHH34Y733ve/G6173uit77QmaxrAd/9Vd/hXA4jNHRUQSDwYH2JtNEb06Sb7VaA1PeKUCYZhUKhQbEBwdK8jV+v1+qJPRvsKWMMNGL4qDZbIp5fa0IarfbkpZFgdHpdFAqldBut6XKw1YyVk4YAsAqCeOUmQTG43Hda0UXDftcC839rMr4fD6k02lMT0+j2WziM5/5DBzH2ehbqiiKoiiKohioUFnhfe97H2ZnZ/EDP/ADuOWWWy7rPb1eD1/4whc2eGXPzl/8xV+gVCph165diEQiUgmgVwSACAxgtb0KWN7Im3HCnPbOigOrL2wBazabqNVqMuiRG35WIYDV6GKa8s0ZJZwSz7Ysy7LEyE4BZc544XkQtmiZLWE8JyZ58efmxHqfz4d2uy2CyhRMAOS8/X4/bNvG0NAQbr31Vti2jc9+9rP49re/vTE3T1EURVEURXlOVKgY/OEf/iFGRkbwqle96rLfU61W8WM/9mMbuKrn5mtf+xrm5uawe/duTExMDFRVzMnt3W5XvCEUJF6vF+12W2J+e70eksnkgBeFAyaBQbM5RQR9LnwPj8P2LXpC2HpFkz1nrPR6PTiOI8cxvSYUEQAGjPTdbhf1el2qPhRbkUhkwFjvOI6IJVZnWGWhsOG8lbGxMezevRv9fh+f//zn8bWvfW3D752iKIqiKIry7KhQMXjkkUfw9a9/HT/0Qz+Ed73rXZf9viuJNt4Ivva1r+HJJ5/E+Pg4du7ciWg0Kr8z44nZCsb1svWLgxu58a/VarKhp+EcwIDXw4waNgdJ9vt98c3Ytg2PxyOG9lAoJBUbJnyZ8b5ro4L5DwWXx+NBKBSSNjEKDwByXrVaTdZq27ZUUhiXTGHjui6i0Simp6dx++23IxKJ4ODBg3jkkUd0uryiKIqiKMo24IYd+PhsfPjDH8bk5CT+2T/7Z3j00UfxzDPPPOfrXdfFX/7lX27S6p6dQ4cO4fDhw3jVq16F3bt3y2wSelFY4aCfIxAIoN1ui7+Fm35WH0KhkPg22CZGv8daoQKsekLoW+GxOOMkFouJz8P0vJjmfnNQJVmbUOb3+2WNprneFFs8DtdBIcVJ9bFYDENDQ/D7/XjyySelemJOr1cURVEURVG2Fq2orKHX6+HP//zPUS6X8eY3v/my3/Oyl71sg1f23NAL8s1vfhMzMzMYHh7G1NQUotHoQFXFFAJs02J7VKvVkgoEqyesQJhVDQoLc74Kj2fGNbdaLWnlCoVC8rtOp4NWq4VgMIharTYwoJEVG4oRYFXYsHrCFDGKK76HvzNFFYVVMpnE3r17ceDAAUQiETz00EP47Gc/i9OnTw+cn6IoiqIoirI9UKFyCY4ePYqvf/3rOHDgAN773vde1nvY0rTV9Ho9PPnkk3jwwQfR6/Wwf/9+TE1NSTsWqyFmZaTZbEpFBABs25ZWMQqTUCg0MHeFLVk8Jgc+rhUY5sR3Vj84nNL0iTD9i8dkm5ophngss1LD9wCQhDG+PhgMYu/evbjjjjtwyy23YHFxEV/84hfxT//0T8jn8wMDMBVFURRFUZTthbZ+XYJer4fPf/7zyGQyePnLX4477rgDTz311HO+59ChQ0gmkyiVSpuzyOeg2+1idnYWuVwOk5OTuPnmmzE1NYVarSbRwazAUDTQ98FWLPpDmJplVioADFROzAQu/o7xwfycer0uCV18D9vEzNQuRiX3+32Zu0KxRFHFyg1FEYWSZVlIp9MAgEwmg2AwiFKphAceeAD9fl/M+ipOFEVRFEVRtj8qVJ6FRqOBL33pSxgbG8PP/dzP4ed+7uee8/WdTgf33HMPvvzlL2/SCp8bRgKfOXMGCwsLGB0dxYte9CIkk0nYto1qtToQK2wa7dea7QFIFYOCwoRJYBQzrHCwYsG5JRzKWK1WYdu2tGyxusO18L0UTKys+P1+RKNRNBoNWJYlE+tt24bP58Pw8DCSySSWlpbwyCOPSEwxqzWKoiiKoijKtYMKlefgwoULePjhh/GOd7wD73jHO/AXf/EXz/n6M2fObM7CroButwvHcdBqtVAoFBCLxXDzzTdjYmIC7XYb1WoVjuNIi5VptAdWBQoN6cFgEAAGPCv8uyk4zJQt/t6c3RIIBNBqtUQgmQlkppmebWCsvgSDQQSDQYTDYSSTSYyPjyMej+PMmTN4/PHHxRdTLpe1cqIoiqIoinINo0LlOeh2u3jooYeQTCbxyle+El/96lcxPz//rK8/d+7ctmn/Wkun00GxWESlUoHjOAiFQhgeHsb4+DhisRjq9TqazSYajYYY781J9gAkPYwtXSYULvSsUGDQL9Lr9RAIBKR9jO1awOrMFMuyxNdCMRIMBkXseL1ehMNh8dacPn0ap06dgt/vh+M4KJfLGi2sKIqiKIpynaBC5Xkol8v4p3/6J8Tjcbzzne/Ef/2v//VZX9tut3HHHXfgscce27ZP83u9HgqFAizLQrlcRjabRSQSwe7du5FMJqVy0Wg0RJiY4oR/p3igT4SVFyaLAatmelZMCJPE6E0JhUIIBoNIJBISLxwKhUTQdLtdFAoFzM/Po1wuy3mUSiU0m83Nv4iKoiiKoijKhqNC5TLIZrN49NFH8ba3vQ27du16zhav2dnZbStSTFzXRa1WQ61Wg9/vR71eRygUQq/Xw9TUFGzbRjweH5gWTzM7/8zWLg5ppFBhGxf9J6yWRCIRESWBQEDavmzbRqPRgOM4WFpakpkv5jT6Wq2GcrmsfhNFURRFUZQbBGs7bKoty9r6RTwP8Xgc9913H6anp/HRj370OV+bSqUumrp+LTEyMoJQKCRiwrZtJBIJGbbIli3GA9NbYoqTcDgsAocJX8ByNaVSqaBer4vZnlHDrVYLpVJJ27c2Edd1ra1eg6IoynYhbqXdl1mv2eplKMoNxcPuV1BxC5fcj2hF5TKpVCo4ePAghoeH8fKXvxwPPPDAs742FApt4srWn2w2O/D3SCSCRCIhrVps6WJFhZUP4rquGOf5+2azKTNQyuUyHMe5ZoWcoiiKoiiKsvGoULkCSqUSvv3tb+Od73wnjh07hnw+f8nXVSqVTV7ZxsIWMUVRFEVRFEXZLHQy/RXQ7/dRLpfRbDbxlre85VlfV6vVEIvFLqo0KIqiKIqiKIpyeahQuUJqtRoeeOAB/ORP/iSmp6ef9XW2batQURRFURRFUZQXiAqVK6TdbuPQoUPI5XJ4+9vf/qyvW1xcRCwWw+joqEx3VxRFURRFURTl8lCh8gJotVo4ePAgXvWqV2FqaupZX7d792784A/+IKanp1WsKIqiKIqiKMoVoELlBdBsNvHlL38ZAPAf/sN/QDKZvOTrnnjiCdx+++1461vfisnJyYGhh4qiKIqiKIqiPDsqVF4A3W4XTz31FGZnZ7F37168613vQjqdRjqdRiaTwdDQEDKZDDKZDKLRKF796lfj9a9/PTKZzFYvXVEURVEURVGuCbQf6QXSbrfx93//9/jlX/5lfP/3fz9e9rKXwev1IplMyqR3ABgbG0OtVsNrX/taHDx4EPl8XgcaKoqiKIqiKMrzoELlBdLtdvGlL30JP/IjP4JerwfbtjE1NYW7774bHo9HEr/K5TIOHjwIv9+Pt7/97Wg0Gjh06NAWr15RFEVRFEVRtjcqVK6CTqeD97///fjABz6AxcVFPPjgg8jlchgeHkYsFkM0GkW5XEaj0UCtVsOePXu0/UtRFEVRFEVRLgP1qFwFruvi9OnTeOKJJzA+Pg6v14vZ2VkUCgU0m03Mzs7i2LFjWFxcRKPRQDgcxm233YbR0dGtXrqiKIqiKIqibGu0onKV9Pt9vP/978fv/d7vYXx8HOfOnUM+n8eOHTvw+OOP4z/9p/+ExcVF2LaNt771rRgbG8Pw8DAWFxe3eumKoiiKoiiKsm3Riso68cu//MvIZrOIRqMoFos4fvw4fvu3fxu5XA4A0Gg0cP/99yObzSIYDG7xahVFURRFURRle6NCZR153/veh0ajAb/fj4cffhhLS0vo9/sAltO/7rjjDuzYsQN+v3+LV6ooiqIoiqIo2xsVKuuI67r41V/9VRQKhQGRAgB33XUXxsfHsbCwgFKptHWLVBRFURRFUZRrAPWorDO9Xg/vf//78eM//uMSUQwADzzwANrtNjqdjs5RURRFURRFUZTnQSsqG0C73caf//mfY+/evQgEAgCAZDIJj8eDbrcL13W3eIWKoiiKoiiKsr3RisoG0Wq1cOzYMXQ6HQDAhQsXBlrBFEVRFEVRFEV5dlSobCAUKQC03UtRFEVRFEVRroDnbf2yLCtkWdYjlmU9aVnWIcuy3r/y892WZT1sWdZJy7L+xrKswMrPgyt/P7ny+10bfA6KoiiKolzn6H5EUW48Lsej0gLwva7rvhjAnQBeb1nWfQB+G8Dvu657E4AigJ9aef1PASiu/Pz3V16nKIqiKIpyNeh+RFFuMJ5XqLjLOCt/9a/84wL4XgB/u/LzPwPwgyt/ftPK37Hy+9dYZvyVoiiKoijKFaL7EUW58bis1C/LsryWZT0BIAvgfgCnAJRc1+2uvOQCgImVP08AOA8AK78vA8is45oVRVEURbkB0f2IotxYXJZQcV2357runQAmAbwUwC1X+8GWZb3bsqyDlmUdvNpjKYqiKIpy/bPR+5EOWld7OEVR1pErmqPium4JwNcAfBeApGVZTA2bBDC78udZAFMAsPL7BID8JY71J67r3uO67j0vbOmKoiiKotyIbNR+xI/gRi9dUZQr4HJSv4Yty0qu/NkG8FoAR7D8H4i3rrzsJwF8euXPn1n5O1Z+/1VXJxwqiqIoinIV6H5EUW48LmeOyjiAP7Msy4tlYfNx13X/wbKswwD+2rKs3wTwOICPrbz+YwD+r2VZJwEUALxtA9atKIqiKMqNhe5HFOUGw9oODxcsy9r6RSjKDYjrupqAoyiKskLcSrsvs16z1ctQlBuKh92voOIWLrkfuSKPiqIoiqIoiqIoymagQkVRFEVRFEVRlG2HChVFURRFURRFUbYdKlQURVEURVEURdl2qFBRFEVRFEVRFGXboUJFURRFURRFUZRthwoVRVEURVEURVG2HSpUFEVRFEVRFEXZdqhQURRFURRFURRl27FdJtPnANQALG31WtaRIej5bGeup/N5oecy7bru8HovRlEU5VpF9yPbnuvpXAA9H/Ks+5FtIVQAwLKsg67r3rPV61gv9Hy2N9fT+VxP56IoirLVXG//Tb2ezud6OhdAz+dy0NYvRVEURVEURVG2HSpUFEVRFEVRFEXZdmwnofInW72AdUbPZ3tzPZ3P9XQuiqIoW8319t/U6+l8rqdzAfR8npdt41FRFEVRFEVRFEUh26mioiiKoiiKoiiKAmAbCBXLsl5vWdYxy7JOWpb177Z6PS8Ey7LOWJb1tGVZT1iWdXDlZ2nLsu63LOvEyr9TW73OZ8OyrP9lWVbWsqxnjJ9dcv3WMh9auV9PWZb1kq1b+aV5lvP5j5Zlza7coycsy3qj8bt/v3I+xyzLet3WrPrZsSxryrKsr1mWddiyrEOWZf3iys+v2XukKIqy3dD9yNaj+xHdj6xlS4WKZVleAH8E4A0ADgD4McuyDmzlmq6Cf+a67p1GLNu/A/AV13X3AfjKyt+3K38K4PVrfvZs638DgH0r/7wbwB9v0hqvhD/FxecDAL+/co/udF338wCw8n17G4DbVt7zP1a+l9uJLoB/47ruAQD3AXjPyrqv5XukKIqybdD9yLbhT6H7Ed2PGGx1ReWlAE66rjvjum4bwF8DeNMWr2m9eBOAP1v5858B+MGtW8pz47ruPwEorPnxs63/TQD+j7vMQwCSlmWNb8pCL5NnOZ9n400A/tp13ZbruqcBnMTy93Lb4LruvOu631n5cxXAEQATuIbvkaIoyjZD9yPbAN2P6H5kLVstVCYAnDf+fmHlZ9caLoAvWZb1mGVZ71752ajruvMrf14AMLo1S3vBPNv6r+V79q9XSo//yyh9X1PnY1nWLgB3AXgY1+c9UhRF2Qqul/9u6n7k2kD3I5fJVguV64VXuq77EiyXuN5jWdarzF+6y9Fq12y82rW+/hX+GMBeAHcCmAfwu1u6mheAZVlRAH8H4H2u61bM310n90hRFEW5OnQ/sv3R/cgVsNVCZRbAlPH3yZWfXVO4rju78u8sgE9huVS3yPLWyr+zW7fCF8Szrf+avGeu6y66rttzXbcP4KNYLadeE+djWZYfy/9R+AvXdT+58uPr6h4piqJsIdfFfzd1P7L975nuR67snLZaqDwKYJ9lWbstywpg2UT0mS1e0xVhWVbEsqwY/wzgnwN4Bsvn8ZMrL/tJAJ/emhW+YJ5t/Z8B8BMrSQ73ASgb5b5ty5qeyDdj+R4By+fzNsuygpZl7cay4euRzV7fc2FZlgXgYwCOuK77e8avrqt7pCiKsoXofmT7cl39f53uR67sHvmucs1Xheu6Xcuy/jWALwLwAvhfruse2so1vQBGAXxq+d7BB+AvXdf9R8uyHgXwccuyfgrAWQA/soVrfE4sy/orAK8GMGRZ1gUAvwHgv+HS6/88gDdi2eRVB/CuTV/w8/As5/Nqy7LuxHI58gyAfwUArusesizr4wAOYznN4j2u6/a2YNnPxSsA/DiApy3LemLlZ7+Ga/geKYqibCd0P7I90P2I7kfWopPpFUVRFEVRFEXZdmx165eiKIqiKIqiKMpFqFBRFEVRFEVRFGXboUJFURRFURRFUZRthwoVRVEURVEURVG2HSpUFEVRFEVRFEXZdqhQURRFURRFURRl26FCRVEURVEURVGUbYcKFUVRFEVRFEVRth3/f1K6AEAv3xyvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "slice_map = {\n",
    "    \"img0035.nii.gz\": 170,\n",
    "    \"img0036.nii.gz\": 230,\n",
    "    \"img0037.nii.gz\": 204,\n",
    "    \"img0038.nii.gz\": 204,\n",
    "    \"img0039.nii.gz\": 204,\n",
    "    \"img0040.nii.gz\": 180,\n",
    "}\n",
    "case_num = 0\n",
    "img_name = os.path.split(val_ds[case_num][\"image_meta_dict\"][\"filename_or_obj\"])[1]\n",
    "img = val_ds[case_num][\"image\"]\n",
    "label = val_ds[case_num][\"label\"]\n",
    "img_shape = img.shape\n",
    "label_shape = label.shape\n",
    "print(f\"image shape: {img_shape}, label shape: {label_shape}\")\n",
    "plt.figure(\"image\", (18, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title(\"image\")\n",
    "plt.imshow(img[0, :, :, slice_map[img_name]].detach().cpu(), cmap=\"gray\")\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title(\"label\")\n",
    "plt.imshow(label[0, :, :, slice_map[img_name]].detach().cpu())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Model, Loss, Optimizer\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "model = UNETR(\n",
    "    in_channels=1,\n",
    "    out_channels=14,\n",
    "    img_size=(96, 96, 96),\n",
    "    feature_size=16,\n",
    "    hidden_size=768,\n",
    "    mlp_dim=3072,\n",
    "    num_heads=12,\n",
    "    pos_embed=\"perceptron\",\n",
    "    norm_name=\"instance\",\n",
    "    res_block=True,\n",
    "    dropout_rate=0.0,\n",
    ").to(device)\n",
    "\n",
    "loss_function = DiceCELoss(to_onehot_y=True, softmax=True)\n",
    "torch.backends.cudnn.benchmark = True\n",
    "optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4, weight_decay=1e-5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Execute a typical PyTorch training process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def validation(epoch_iterator_val):\n",
    "    model.eval()\n",
    "    dice_vals = list()\n",
    "    with torch.no_grad():\n",
    "        for step, batch in enumerate(epoch_iterator_val):\n",
    "            val_inputs, val_labels = (batch[\"image\"].cuda(), batch[\"label\"].cuda())\n",
    "            val_outputs = sliding_window_inference(val_inputs, (96, 96, 96), 4, model)\n",
    "            val_labels_list = decollate_batch(val_labels)\n",
    "            val_labels_convert = [\n",
    "                post_label(val_label_tensor) for val_label_tensor in val_labels_list\n",
    "            ]\n",
    "            val_outputs_list = decollate_batch(val_outputs)\n",
    "            val_output_convert = [\n",
    "                post_pred(val_pred_tensor) for val_pred_tensor in val_outputs_list\n",
    "            ]\n",
    "            dice_metric(y_pred=val_output_convert, y=val_labels_convert)\n",
    "            dice = dice_metric.aggregate().item()\n",
    "            dice_vals.append(dice)\n",
    "            epoch_iterator_val.set_description(\n",
    "                \"Validate (%d / %d Steps) (dice=%2.5f)\" % (global_step, 10.0, dice)\n",
    "            )\n",
    "        dice_metric.reset()\n",
    "    mean_dice_val = np.mean(dice_vals)\n",
    "    return mean_dice_val\n",
    "\n",
    "\n",
    "def train(global_step, train_loader, dice_val_best, global_step_best):\n",
    "    model.train()\n",
    "    epoch_loss = 0\n",
    "    step = 0\n",
    "    epoch_iterator = tqdm(\n",
    "        train_loader, desc=\"Training (X / X Steps) (loss=X.X)\", dynamic_ncols=True\n",
    "    )\n",
    "    for step, batch in enumerate(epoch_iterator):\n",
    "        step += 1\n",
    "        x, y = (batch[\"image\"].cuda(), batch[\"label\"].cuda())\n",
    "        logit_map = model(x)\n",
    "        loss = loss_function(logit_map, y)\n",
    "        loss.backward()\n",
    "        epoch_loss += loss.item()\n",
    "        optimizer.step()\n",
    "        optimizer.zero_grad()\n",
    "        epoch_iterator.set_description(\n",
    "            \"Training (%d / %d Steps) (loss=%2.5f)\" % (global_step, max_iterations, loss)\n",
    "        )\n",
    "        if (\n",
    "            global_step % eval_num == 0 and global_step != 0\n",
    "        ) or global_step == max_iterations:\n",
    "            epoch_iterator_val = tqdm(\n",
    "                val_loader, desc=\"Validate (X / X Steps) (dice=X.X)\", dynamic_ncols=True\n",
    "            )\n",
    "            dice_val = validation(epoch_iterator_val)\n",
    "            epoch_loss /= step\n",
    "            epoch_loss_values.append(epoch_loss)\n",
    "            metric_values.append(dice_val)\n",
    "            if dice_val > dice_val_best:\n",
    "                dice_val_best = dice_val\n",
    "                global_step_best = global_step\n",
    "                torch.save(\n",
    "                    model.state_dict(), os.path.join(root_dir, \"best_metric_model.pth\")\n",
    "                )\n",
    "                print(\n",
    "                    \"Model Was Saved ! Current Best Avg. Dice: {} Current Avg. Dice: {}\".format(\n",
    "                        dice_val_best, dice_val\n",
    "                    )\n",
    "                )\n",
    "            else:\n",
    "                print(\n",
    "                    \"Model Was Not Saved ! Current Best Avg. Dice: {} Current Avg. Dice: {}\".format(\n",
    "                        dice_val_best, dice_val\n",
    "                    )\n",
    "                )\n",
    "        global_step += 1\n",
    "    return global_step, dice_val_best, global_step_best\n",
    "\n",
    "\n",
    "max_iterations = 25000\n",
    "eval_num = 500\n",
    "post_label = AsDiscrete(to_onehot=True, n_classes=14)\n",
    "post_pred = AsDiscrete(argmax=True, to_onehot=True, n_classes=14)\n",
    "dice_metric = DiceMetric(include_background=True, reduction=\"mean\", get_not_nans=False)\n",
    "global_step = 0\n",
    "dice_val_best = 0.0\n",
    "global_step_best = 0\n",
    "epoch_loss_values = []\n",
    "metric_values = []\n",
    "while global_step < max_iterations:\n",
    "    global_step, dice_val_best, global_step_best = train(\n",
    "        global_step, train_loader, dice_val_best, global_step_best\n",
    "    )\n",
    "model.load_state_dict(torch.load(os.path.join(root_dir, \"best_metric_model.pth\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train completed, best_metric: 0.7891 at iteration: 23500\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    f\"train completed, best_metric: {dice_val_best:.4f} \"\n",
    "    f\"at iteration: {global_step_best}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the loss and metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(\"train\", (12, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title(\"Iteration Average Loss\")\n",
    "x = [eval_num * (i + 1) for i in range(len(epoch_loss_values))]\n",
    "y = epoch_loss_values\n",
    "plt.xlabel(\"Iteration\")\n",
    "plt.plot(x, y)\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title(\"Val Mean Dice\")\n",
    "x = [eval_num * (i + 1) for i in range(len(metric_values))]\n",
    "y = metric_values\n",
    "plt.xlabel(\"Iteration\")\n",
    "plt.plot(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check best model output with the input image and label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1296x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "case_num = 4\n",
    "model.load_state_dict(torch.load(os.path.join(root_dir, \"best_metric_model.pth\")))\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    img_name = os.path.split(val_ds[case_num][\"image_meta_dict\"][\"filename_or_obj\"])[1]\n",
    "    img = val_ds[case_num][\"image\"]\n",
    "    label = val_ds[case_num][\"label\"]\n",
    "    val_inputs = torch.unsqueeze(img, 1).cuda()\n",
    "    val_labels = torch.unsqueeze(label, 1).cuda()\n",
    "    val_outputs = sliding_window_inference(\n",
    "        val_inputs, (96, 96, 96), 4, model, overlap=0.8\n",
    "    )\n",
    "    plt.figure(\"check\", (18, 6))\n",
    "    plt.subplot(1, 3, 1)\n",
    "    plt.title(\"image\")\n",
    "    plt.imshow(val_inputs.cpu().numpy()[0, 0, :, :, slice_map[img_name]], cmap=\"gray\")\n",
    "    plt.subplot(1, 3, 2)\n",
    "    plt.title(\"label\")\n",
    "    plt.imshow(val_labels.cpu().numpy()[0, 0, :, :, slice_map[img_name]])\n",
    "    plt.subplot(1, 3, 3)\n",
    "    plt.title(\"output\")\n",
    "    plt.imshow(\n",
    "        torch.argmax(val_outputs, dim=1).detach().cpu()[0, :, :, slice_map[img_name]]\n",
    "    )\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleanup data directory\n",
    "\n",
    "Remove directory if a temporary was used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "if directory is None:\n",
    "    shutil.rmtree(root_dir)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
